satellite-image-deep-learning

This page lists resources for performing deep learning on satellite imagery. To a lesser extent classical Machine learning (e.g. random forests) are also discussed, as are classical image processing techniques. Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities. If you find this work useful please give it a star and consider sponsoring it. You can also follow me on the platforms below where I aim to post daily updates on my new discoveries. Thanks!

Techniques

This section explores the different deep and machine learning (ML) techniques applied to common problems in satellite imagery analysis. Good background reading is Deep learning in remote sensing applications: A meta-analysis and review

Classification

The classic cats vs dogs image classification task, which in the remote sensing domain is used to assign a label to an image, e.g. this is an image of a forest. The more complex case is applying multiple labels to an image. This approach of image level classification is not to be confused with pixel-level classification which is called semantic segmentation. In general, aerial images cover large geographical areas that include multiple classes of land, so treating this is as a classification problem is less common than using semantic segmentation. I recommend to get started with the EuroSAT dataset.

Land classification on Sentinel 2 data using a simple sklearn cluster algorithm or deep learning CNN
Land Use Classification on Merced dataset using CNN in Keras or fastai. Also checkout Multi-label Land Cover Classification using the redesigned multi-label Merced dataset with 17 land cover classes
Multi-Label Classification of Satellite Photos of the Amazon Rainforest using keras or FastAI
Detecting Informal Settlements from Satellite Imagery using fine-tuning of ResNet-50 classifier with repo
Vision Transformers Use Case: Satellite Image Classification without CNNs
Land-Cover-Classification-using-Sentinel-2-Dataset -> well written Medium article accompanying this repo but using the EuroSAT dataset
Land Cover Classification of Satellite Imagery using Convolutional Neural Networks using Keras and a multi spectral dataset captured over vineyard fields of Salinas Valley, California
Detecting deforestation from satellite images -> using FastAI and ResNet50, with repo fsdl_deforestation_detection
Neural Network for Satellite Data Classification Using Tensorflow in Python -> A step-by-step guide for Landsat 5 multispectral data classification for binary built-up/non-built-up class prediction, with repo
Slums mapping from pretrained CNN network on VHR (Pleiades: 0.5m) and MR (Sentinel: 10m) imagery
Comparing urban environments using satellite imagery and convolutional neural networks -> includes interesting study of the image embedding features extracted for each image on the Urban Atlas dataset. Accompanying paper
RSI-CB -> A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data. See also Remote-sensing-image-classification
NAIP_PoolDetection -> modelled as an object recognition problem, a CNN is used to identify images as being swimming pools or something else - specifically a street, rooftop, or lawn
Land Use and Land Cover Classification using a ResNet Deep Learning Architecture -> uses fastai and the EuroSAT dataset
Vision Transformers Use Case: Satellite Image Classification without CNNs
WaterNet -> a CNN that identifies water in satellite images
Road-Network-Classification -> Road network classification model using ResNet-34, road classes organic, gridiron, radial and no pattern
Scaling AI to map every school on the planet
Landsat classification CNN tutorial with repo
satellite-crosswalk-classification
Understanding the Amazon Rainforest with Multi-Label Classification + VGG-19, Inceptionv3, AlexNet & Transfer Learning
Implementation of the 3D-CNN model for land cover classification -> uses the Sundarbans dataset, with repo. Also read Land cover classification of Sundarbans satellite imagery using K-Nearest Neighbor(K-NNC), Support Vector Machine (SVM), and Gradient Boosting classification algorithms which is by the same author and shares the repo
SSTN -> PyTorch Implementation of SSTNs for hyperspectral image classifications from the IEEE T-GRS paper "Spectral-Spatial Transformer Network for Hyperspectral Image Classification: A FAS Framework." Demonstrates a novel spectral-spatial transformer network (SSTN), which consists of spatial attention and spectral association modules, to overcome the constraints of convolution kernels
SatellitePollutionCNN -> A novel algorithm to predict air pollution levels with state-of-art accuracy using deep learning and GoogleMaps satellite images
PropertyClassification -> Classifying the type of property given Real Estate, satellite and Street view Images
remote-sense-quickstart -> classification on a number of datasets, including with attention visualization
Satellite image classification using multiple machine learning algorithms
satsense -> a Python library for land use/cover classification using classical features including HoG & NDVI
PyTorch_UCMerced_LandUse -> simple pytorch implementation fine tuned on ResNet and basic augmentations
EuroSAT-image-classification -> simple pytorch implementation fine tuned on ResNet
landcover_classification -> using fast.ai on EuroSAT
IGARSS2020_BWMS -> Band-Wise Multi-Scale CNN Architecture for Remote Sensing Image Scene Classification with a novel CNN architecture for the feature embedding of high-dimensional RS images
image.classification.on.EuroSAT -> solution in pure pytorch
hurricane_damage -> Post-hurricane structure damage assessment based on aerial imagery with CNN
openai-drivendata-challenge -> Using deep learning to classify the building material of rooftops (aerial imagery from South America)
is-it-abandoned -> Can we tell if a house is abandoned based on aerial LIDAR imagery?
BoulderAreaDetector -> CNN to classify whether a satellite image shows an area would be a good rock climbing spot or not
ISPRS_S2FL -> code for paper: Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model. S2FL is capable of decomposing multimodal RS data into modality-shared and modality-specific components, enabling the information blending of multi-modalities more effectively
Brazilian-Coffee-Detection -> uses Keras with public dataset
tf-crash-severity -> predict the crash severity for given road features contained within satellite images
ensemble_LCLU -> code for 2021 paper: Deep neural network ensembles for remote sensing land cover and land use classification
cerraNet -> contextually classify the types of use and coverage in the Brazilian Cerrado
Urban-Analysis-Using-Satellite-Imagery -> classify urban area as planned or unplanned using a combination of segmentation and classification
ChipClassification -> code for 2019 paper: Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery
DeeplearningClassficationLandsat-tImages -> Water/Ice/Land Classification Using Large-Scale Medium Resolution Landsat Satellite Images
wildfire-detection-from-satellite-images-ml -> detect whether an image contains a wildfire, with example flask web app
mining-discovery-with-deep-learning -> code for the 2020 paper: Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning
e-Farmerce-platform -> classify crop type
sentinel2-deep-learning -> Novel Training Methodologies for Land Classification of Sentinel-2 Imagery
RSSC-transfer -> code for 2021 paper: The Role of Pre-Training in High-Resolution Remote Sensing Scene Classification
Classifying Geo-Referenced Photos and Satellite Images for Supporting Terrain Classification -> detect floods
Pay-More-Attention -> code for 2021 paper: Remote Sensing Image Scene Classification Based on an Enhanced Attention Module
Remote-Sensing-Image-Classification-via-Improved-Cross-Entropy-Loss-and-Transfer-Learning-Strategy -> code for 2019 paper: Remote Sensing Image Classification via Improved Cross-Entropy Loss and Transfer Learning Strategy Based on Deep Convolutional Neural Networks
DenseNet40-for-HRRSISC -> DenseNet40 for remote sensing image scene classification, uses UC Merced Dataset
SKAL -> code for 2022 paper: Looking Closer at the Scene: Multiscale Representation Learning for Remote Sensing Image Scene Classification
potsdam-tensorflow-practice -> image classification of Potsdam dataset using tensorflow
SAFF -> code for 2021 paper: Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification
GLNET -> code for 2021 paper: Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments
Remote-sensing-image-classification -> transfer learning using pytorch to classify remote sensing data into three classes: aircrafts, ships, none
remote_sensing_pretrained_models -> as an alternative to fine tuning on models pretrained on ImageNet, here some CNN are pretrained on the RSD46-WHU & AID datasets
CNN_AircraftDetection -> CNN for aircraft detection in satellite images using keras
OBIC-GCN -> code for 2021 paper: Object-based Classification Framework of Remote Sensing Images with Graph Convolutional Networks

Segmentation

Segmentation will assign a class label to each pixel in an image. Segmentation is typically grouped into semantic, instance or panoptic segmentation. In semantic segmentation objects of the same class are assigned the same label, whilst in instance segmentation each object is assigned a unique label. Panoptic segmentation combines instance and semantic predictions. Read this beginner’s guide to segmentation. Single class models are often trained for road or building segmentation, with multi class for land use/crop type classification. Image annotation can take longer than for object detection since every pixel must be annotated. Note that many articles which refer to 'hyperspectral land classification' are actually describing semantic segmentation. Note that cloud detection can be addressed with semantic segmentation and has its own section Cloud detection & removal

awesome-satellite-images-segmentation
Satellite Image Segmentation: a Workflow with U-Net is a decent intro article
mmsegmentation -> Semantic Segmentation Toolbox with support for many remote sensing datasets including LoveDA , Potsdam, Vaihingen & iSAID
How to create a DataBlock for Multispectral Satellite Image Semantic Segmentation using Fastai
Using a U-Net for image segmentation, blending predicted patches smoothly is a must to please the human eye -> python code to blend predicted patches smoothly. See Satellite-Image-Segmentation-with-Smooth-Blending
DCA -> code for 2022 paper: Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation
SCAttNet -> Semantic Segmentation Network with Spatial and Channel Attention Mechanism
unetseg -> A set of classes and CLI tools for training a semantic segmentation model based on the U-Net architecture, using Tensorflow and Keras. This implementation is tuned specifically for satellite imagery and other geospatial raster data
Semantic Segmentation of Satellite Imagery using U-Net & fast.ai -> with repo
clusternet_segmentation -> Unsupervised Segmentation by applying K-Means clustering to the features generated by Neural Network
Collection of different Unet Variant -> demonstrates VggUnet, ResUnet, DenseUnet, Unet. AttUnet, MobileNetUnet, NestedUNet, R2AttUNet, R2UNet, SEUnet, scSEUnet, Unet_Xception_ResNetBlock, in keras
Efficient-Transformer -> code for 2021 paper: Efficient Transformer for Remote Sensing Image Segmentation
weakly_supervised -> code for the 2020 paper: Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery
HRCNet-High-Resolution-Context-Extraction-Network -> code to 2021 paper: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images
Semantic segmentation of SAR images using a self supervised technique
satellite-segmentation-pytorch -> explores a wide variety of image augmentations to increase training dataset size
IEEE_TGRS_SpectralFormer -> code for 2021 paper: Spectralformer: Rethinking hyperspectral image classification with transformers
Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels -> code for 2022 paper
Semantic-Segmentation-with-Sparse-Labels -> codes and data for learning from sparse annotations
SNDF -> code for 2020 paper: Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation
Satellite-Image-Classification -> using random forest or support vector machines (SVM) and sklearn
dynamic-rs-segmentation -> code for 2019 paper: Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks
Remote-sensing-image-semantic-segmentation-tf2 -> remote sensing image semantic segmentation repository based on tf.keras includes backbone networks such as resnet, densenet, mobilenet, and segmentation networks such as deeplabv3+, pspnet, panet, and refinenet
segmentation_models.pytorch -> Segmentation models with pretrained backbones, has been used in multiple winning solutions to remote sensing competitions
SSRN -> code for 2017 paper: Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework
SO-DNN -> code for 2021 paper: Simplified object-based deep neural network for very high resolution remote sensing image classification
SANet -> code for 2019 paper: Scale-Aware Network for Semantic Segmentation of High-Resolution Aerial Images
aerial-segmentation -> code for 2017 paper: Learning Aerial Image Segmentation from Online Maps
IterativeSegmentation -> code for 2016 paper: Recurrent Neural Networks to Correct Satellite Image Classification Maps
Detectron2 FPN + PointRend Model for amazing Satellite Image Segmentation -> 15% increase in accuracy when compared to the U-Net model
HybridSN -> code for 2019 paper: HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification
TNNLS_2022_X-GPN -> code for 2022 paper: Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification
singleSceneSemSegTgrs2022 -> code for 2022 paper: Unsupervised Single-Scene Semantic Segmentation for Earth Observation
A-Fast-and-Compact-3-D-CNN-for-HSIC -> code for 2020 paper: A Fast and Compact 3-D CNN for Hyperspectral Image Classification
HSNRS -> code for 2017 paper: Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery
GiGCN -> code for 2022 paper: Graph-in-Graph Convolutional Network for Hyperspectral Image Classification
SSAN -> code for 2019 paper: Spectral-Spatial Attention Networks for Hyperspectral Image Classification
drone-images-semantic-segmentation -> Multiclass Semantic Segmentation of Aerial Drone Images Using Deep Learning
Satellite-Image-Segmentation-with-Smooth-Blending -> uses Smoothly-Blend-Image-Patches
BayesianUNet -> Pytorch Bayesian UNet model for segmentation and uncertainty prediction, applied to the Potsdam Dataset
RAANet -> code for 2022 paper: RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images
wheelRuts_semanticSegmentation -> code for 2022 paper: Mapping wheel-ruts from timber harvesting operations using deep learning techniques in drone imagery
LWN-for-UAVRSI -> Light-Weight Semantic Segmentation Network for UAV Remote Sensing Images, applied to Vaihingen, UAVid and UDD6 datasets
hypernet -> library which implements; accurate hyperspectral image (HSI) segmentation and analysis using deep neural networks, optimization of deep neural network architectures for hyperspectral data segmentation, hyperspectral data augmentation, validation of existent and emerging HSI segmentation algorithms, simulation of multispectral data using HSI

Segmentation - Land use & land cover

nga-deep-learning -> performs semantic segmentation on high resultion GeoTIF data using a modified U-Net & Keras, published by NASA researchers
Automatic Detection of Landfill Using Deep Learning
SpectralNET -> a 2D wavelet CNN for Hyperspectral Image Classification, uses Salinas Scene dataset & Keras
laika -> The goal of this repo is to research potential sources of satellite image data and to implement various algorithms for satellite image segmentation
PEARL -> a human-in-the-loop AI tool to drastically reduce the time required to produce an accurate Land Use/Land Cover (LULC) map, blog post, uses Microsoft Planetary Computer and ML models run locally in the browser. Code for backelnd and frontend
Land Cover Classification with U-Net -> Satellite Image Multi-Class Semantic Segmentation Task with PyTorch Implementation of U-Net, uses DeepGlobe Land Cover Segmentation dataset, with code
Multi-class semantic segmentation of satellite images using U-Net using DSTL dataset, tensorflow 1 & python 2.7. Accompanying article
Codebase for multi class land cover classification with U-Net accompanying a masters thesis, uses Keras
dubai-satellite-imagery-segmentation -> due to the small dataset, image augmentation was used
U-Net for Semantic Segmentation on Unbalanced Aerial Imagery -> using the Dubai dataset
CDL-Segmentation -> code for the 2021 paper: Deep Learning Based Land Cover and Crop Type Classification: A Comparative Study. Compares UNet, SegNet & DeepLabv3+
LoveDA -> code for the 2021 paper: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
Satellite Imagery Semantic Segmentation with CNN -> 7 different segmentation classes, DeepGlobe Land Cover Classification Challenge dataset, with repo
Aerial Semantic Segmentation using U-Net Deep Learning Model medium article, with repo
UNet-Satellite-Image-Segmentation -> A Tensorflow implentation of light UNet semantic segmentation framework
DeepGlobe Land Cover Classification Challenge solution
Semantic-segmentation-with-PyTorch-Satellite-Imagery -> predict 25 classes on RGB imagery taken to assess the damage after Hurricane Harvey
Semantic Segmentation With Sentinel-2 Imagery -> uses LandCoverNet dataset and fast.ai
CNN_Enhanced_GCN -> code for 2021 paper: CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification
LULCMapping-WV3images-CORINE-DLMethods -> Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images
SOLC -> code for 2022 paper: MCANet: A joint semantic segmentation framework of optical and SAR images for land use classification. Uses WHU-OPT-SAR-dataset
MUnet-LUC -> Land Use with mUnet
land-cover -> code for 2021 paper: Model Generalization in Deep Learning Applications for Land Cover Mapping
generalizablersc -> code for 2022 paper: Cross-dataset Learning for Generalizable Land Use Scene Classification
Large-scale-Automatic-Identification-of-Urban-Vacant-Land -> code for 2022 paper: Large-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images
SSLTransformerRS -> code for 2022 paper: Self-supervised Vision Transformers for Land-cover Segmentation and Classification
aerial-tile-segmentation -> Large satellite image semantic segmentation into 6 classes using Tensorflow 2.0 and ISPRS benchmark dataset

Segmentation - Vegetation, crops & crop boundaries

Сrор field boundary detection: approaches overview and main challenges - review article, no code
Сrор field boundary detection: approaches and main challenges -> Medium article, covering historical approaches to present dat
kenya-crop-mask -> Annual and in-season crop mapping in Kenya - LSTM classifier to classify pixels as containing crop or not, and a multi-spectral forecaster that provides a 12 month time series given a partial input. Dataset downloaded from GEE and pytorch lightning used for training
What’s growing there? Identify crops from multi-spectral remote sensing data (Sentinel 2) using eo-learn for data pre-processing, cloud detection, NDVI calculation, image augmentation & fastai
Tree species classification from from airborne LiDAR and hyperspectral data using 3D convolutional neural networks accompanies research paper and uses fastai
crop-type-classification -> using Sentinel 1 & 2 data with a U-Net + LSTM, more features (i.e. bands) and higher resolution produced better results (article, no code)
Find sports fields using Mask R-CNN and overlay on open-street-map
An LSTM to generate a crop mask for Togo
DeepSatModels -> Code for paper "Context-self contrastive pretraining for crop type semantic segmentation"
farm-pin-crop-detection-challenge -> Using eo-learn and fastai to identify crops from multi-spectral remote sensing data
Detecting Agricultural Croplands from Sentinel-2 Satellite Imagery -> We developed UNet-Agri, a benchmark machine learning model that classifies croplands using open-access Sentinel-2 imagery at 10m spatial resolution
DeepTreeAttention -> Implementation of Hang et al. 2020 "Hyperspectral Image Classification with Attention Aided CNNs" for tree species prediction
Crop-Classification -> crop classification using multi temporal satellite images
ParcelDelineation -> using a French polygons dataset and unet in keras
crop-mask -> End-to-end workflow for generating high resolution cropland maps, uses GEE & LSTM model
DeepCropMapping -> A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping, uses LSTM
Segment Canopy Cover and Soil using NDVI and Rasterio
Use KMeans clustering to segment satellite imagery by land cover/land use
ResUnet-a -> Implementation of the paper "ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data" in TensorFlow
DSD_paper_2020 -> The code for the paper: Crop Type Classification based on Machine Learning with Multitemporal Sentinel-1 Data
MR-DNN -> extract rice field from Landsat 8 satellite imagery
deep_learning_forest_monitoring -> Estimate vegetation height, code for paper: Forest mapping and monitoring of the African continent using Sentinel-2 data and deep learning
global-cropland-mapping -> global multi-temporal cropland mapping
U-Net for Semantic Segmentation of Soyabean Crop Fields with SAR images
UNet-RemoteSensing -> uses 7 bands of Landsat and keras
Landuse_DL -> delineate landforms due to the thawing of ice-rich permafrost
canopy -> code for 2019 paper: A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery
RandomForest-Classification -> script is for random forest classification of remote sensing multi-band images, used in 2019 paper: Multisensor data to derive peatland vegetation communities using a fixed-wing unmanned aerial vehicle
forest_change_detection -> forest change segmentation with time-dependent models, including Siamese, UNet-LSTM, UNet-diff, UNet3D models. Code for 2021 paper: Deep Learning for Regular Change Detection in Ukrainian Forest Ecosystem With Sentinel-2
cultionet -> segmentation of cultivated land, built on PyTorch Geometric and PyTorch Lightning
sentinel-tree-cover -> code for 2020 paper: A global method to identify trees outside of closed-canopy forests with medium-resolution satellite imagery

Segmentation - Water, coastlines & floods

UNSOAT used fastai to train a Unet to perform semantic segmentation on satellite imageries to detect water - paper + notebook, accuracy 0.97, precision 0.91, recall 0.92
Semi-Supervised Classification and Segmentation on High Resolution Aerial Images - Solving the FloodNet problem
Flood Detection and Analysis using UNET with Resnet-34 as the back bone uses fastai
Houston_flooding -> labeling each pixel as either flooded or not using data from Hurricane Harvey. Dataset consisted of pre and post flood images, and a ground truth floodwater mask was created using unsupervised clustering (with DBScan) of image pixels with human cluster verification/adjustment
ml4floods -> An ecosystem of data, models and code pipelines to tackle flooding with ML
A comprehensive guide to getting started with the ETCI Flood Detection competition -> using Sentinel1 SAR & pytorch
Map Floodwater of SAR Imagery with SageMaker -> applied to Sentinel-1 dataset
1st place solution for STAC Overflow: Map Floodwater from Radar Imagery hosted by Microsoft AI for Earth -> combines Unet with Catboostclassifier, taking their maxima, not the average
hydra-floods -> an open source Python application for downloading, processing, and delivering surface water maps derived from remote sensing data
CoastSat -> tool for mapping coastlines which has an extension CoastSeg using segmentation models
Satellite_Flood_Segmentation_of_Harvey -> explores both deep learning and traditional kmeans
Flood Event Detection Utilizing Satellite Images
ETCI-2021-Competition-on-Flood-Detection -> Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training, with arxiv paper
FDSI -> Flood Detection in Satellite Images - 2017 Multimedia Satellite Task
deepwatermap -> a deep model that segments water on multispectral images
rivamap -> an automated river analysis and mapping engine
deep-water -> track changes in water level
WatNet -> A deep ConvNet for surface water mapping based on Sentinel-2 image, uses the Earth Surface Water Dataset
A-U-Net-for-Flood-Extent-Mapping -> in keras
floatingobjects -> code for the paper: TOWARDS DETECTING FLOATING OBJECTS ON A GLOBAL SCALE WITHLEARNED SPATIAL FEATURES USING SENTINEL 2. Uses U-Net & pytorch
River-Network-Extraction-from-Satellite-Image-using-UNet-and-Tensorflow -> uses Sentinel-2 imagery
SpaceNet8 -> baseline Unet solution to detect flooded roads and buildings. With paper
dlsim -> code for 2020 paper: Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping

Segmentation - Fire, smoke & burn areas

Wild Fire Detection using U-Net trained on Databricks & Keras, semantic segmentation
A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS with code. Dataset created on Google Earth Engine, downloaded to local machine for model training using fastai. The BA-Net model used is much smaller than U-Net, resulting in lower memory requirements and a faster computation
AI Geospatial Wildfire Risk Prediction -> A predictive model using geospatial raster data to asses wildfire hazard potential over the contiguous United States using Unet
IndustrialSmokePlumeDetection -> using Sentinel-2 & a modified ResNet-50
burned-area-detection -> uses Sentinel-2
rescue -> code of the paper: Attention to fires: multi-channel deep-learning models forwildfire severity prediction
smoke_segmentation -> Segmenting smoke plumes and predicting density from GOES imagery
wildfire-detection -> Using Vision Transformers for enhanced wildfire detection in satellite images
Burned_Area_Detection -> Detecting Burned Areas with Sentinel-2 data

Segmentation - Landslides

landslide4sense -> a competition focused on landslide detection using globally distributed multi-source satellite imagery. Baseline solution unet
landslide-mapping-with-cnn -> code for 2021 paper: A new strategy to map landslides with a generalized convolutional neural network

Segmentation - Glaciers

HED-UNet -> a model for simultaneous semantic segmentation and edge detection, examples provided are glacier fronts and building footprints using the Inria Aerial Image Labeling dataset
glacier_mapping -> Mapping glaciers in the Hindu Kush Himalaya, Landsat 7 images, Shapefile labels of the glaciers, Unet with dropout
glacier-detect-ML -> a simple logistic regression model to identify a glacier in Landsat satellite imagery
GlacierSemanticSegmentation -> uses unet

Segmentation - Other environmental

Detection of Open Landfills -> uses Sentinel-2 to detect large changes in the Normalized Burn Ratio (NBR)
sea_ice_remote_sensing -> Sea Ice Concentration classification
Methane-detection-from-hyperspectral-imagery -> code for 2020 paper: Deep Remote Sensing Methods for Methane Detection in Overhead Hyperspectral Imagery
EddyNet -> A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies

Segmentation - Roads

Extracting roads is challenging due to the occlusions caused by other objects and the complex traffic environment

Semantic segmentation of roads and highways using Sentinel-2 imagery (10m) super-resolved using the SENX4 model up to x4 the initial spatial resolution (2.5m) (results, no repo)
Semantic Segmentation of roads using U-net Keras, OSM data, project summary article by student, no code
Road detection using semantic segmentation and albumentations for data augmention using the Massachusetts Roads Dataset, U-net & Keras. With code
Winning Solutions from SpaceNet Road Detection and Routing Challenge
RoadVecNet -> Road-Network-Segmentation-and-Vectorization in keras with dataset and paper
Detecting road and road types jupyter notebook
awesome-deep-map -> A curated list of resources dedicated to deep learning / computer vision algorithms for mapping. The mapping problems include road network inference, building footprint extraction, etc.
RoadTracer: Automatic Extraction of Road Networks from Aerial Images -> uses an iterative search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN
road_detection_mtl -> Road Detection using a multi-task Learning technique to improve the performance of the road detection task by incorporating prior knowledge constraints, uses the SpaceNet Roads Dataset
road_connectivity -> Improved Road Connectivity by Joint Learning of Orientation and Segmentation (CVPR2019)
Road-Network-Extraction using classical Image processing -> blur & canny edge detection
SPIN_RoadMapper -> Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving
road_extraction_remote_sensing -> pytorch implementation, CVPR2018 DeepGlobe Road Extraction Challenge submission. See also DeepGlobe-Road-Extraction-Challenge
RoadDetections dataset by Microsoft
CoANet -> Connectivity Attention Network for Road Extraction From Satellite Imagery. The CoA module incorporates graphical information to ensure the connectivity of roads are better preserved. With paper
ML_EPFL_Project_2 -> U-Net in Pytorch to perform semantic segmentation of roads on satellite images
Satellite Imagery Road Segmentation -> intro articule on Medium using the kaggle Massachusetts Roads Dataset
Label-Pixels -> for semantic segmentation of roads and other features
Satellite-image-road-extraction -> code for 2018 paper: Road Extraction by Deep Residual U-Net
road_building_extraction -> Pytorch implementation of U-Net architecture for road and building extraction
Satellite-Imagery-Road-Extraction -> research project in keras
SGCN -> code for 2021 paper: Split Depth-Wise Separable Graph-Convolution Network for Road Extraction in Complex Environments From High-Resolution Remote-Sensing Images
ASPN -> code for 2020 paper: Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks
FCNs-for-road-extraction-keras -> Road extraction of high-resolution remote sensing images based on various semantic segmentation networks
cresi -> Road network extraction from satellite imagery, with speed and travel time estimates
road-extraction-d-linknet -> code for 2018 paper: D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction
Sat2Graph -> code for 2020 paper: Road Graph Extraction through Graph-Tensor Encoding
Image-Segmentation) -> using Massachusetts Road dataset and fast.ai
RoadTracer-M -> code for 2019 paper: Road Network Extraction from Satellite Images Using CNN Based Segmentation and Tracing
ScRoadExtractor -> code for 2020 paper: Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images
RoadDA -> code for 2021 paper: Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images
DeepSegmentor -> A Pytorch implementation of DeepCrack and RoadNet projects
Cascade_Residual_Attention_Enhanced_for_Refinement_Road_Extraction -> code for 2021 paper: Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images
nia-road-baseline -> code for 2020 paper: NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local Operations
IRSR-net -> code for 2022 paper: Lightweight Remote Sensing Road Detection Network
hironex -> A python tool for automatic, fully unsupervised extraction of historical road networks from historical maps

Segmentation - Buildings & rooftops

Semantic Segmentation on Aerial Images using fastai uses U-Net on the Inria Aerial Image Labeling Dataset of urban settlements in Europe and the United States, and is labelled as a building and not building classes (no repo)
Road and Building Semantic Segmentation in Satellite Imagery uses U-Net on the Massachusetts Roads Dataset & keras
find-unauthorized-constructions-using-aerial-photography -> semantic segmentation using U-Net with custom_f1 metric & Keras. The creation of the dataset is described in this article
semantic segmentation model to identify newly developed or flooded land using NAIP imagery provided by the Chesapeake Conservancy, training on MS Azure
Pix2Pix-for-Semantic-Segmentation-of-Satellite-Images -> using Pix2Pix GAN network to segment the building footprint from Satellite Images, uses tensorflow
SpaceNetUnet -> Baseline model is U-net like, applied to SpaceNet Vegas data, using Keras
Building footprint detection with fastai on the challenging SpaceNet7 dataset uses U-Net
automated-building-detection -> Input: very-high-resolution (<= 0.5 m/pixel) RGB satellite images. Output: buildings in vector format (geojson), to be used in digital map products. Built on top of robosat and robosat.pink.
project_sunroof_india -> Analyzed Google Satellite images to generate a report on individual house rooftop's solar power potential, uses a range of classical computer vision techniques (e.g Canny Edge Detection) to segment the roofs
JointNet-A-Common-Neural-Network-for-Road-and-Building-Extraction
Mapping Africa’s Buildings with Satellite Imagery: Google AI blog post
nz_convnet -> A U-net based ConvNet for New Zealand imagery to classify building outlines
polycnn -> End-to-End Learning of Polygons for Remote Sensing Image Classification
spacenet_building_detection solution by motokimura using Unet
How to extract building footprints from satellite images using deep learning
Vec2Instance -> applied to the SpaceNet challenge AOI 2 (Vegas) building footprint dataset, tensorflow v1.12
EarthquakeDamageDetection -> Buildings segmentation from satellite imagery and damage classification for each build, using Keras
Semantic-segmentation repo by fuweifu-vtoo -> uses pytorch and the Massachusetts Buildings & Roads Datasets
Extracting buildings and roads from AWS Open Data using Amazon SageMaker -> uses merged RGB (SpaceNet) and LiDAR (USGS 3DEP) datasets with Unet to reproduce the winning algorithm from SpaceNet challenge 4 by XD_XD. With repo
TF-SegNet -> AirNet is a segmentation network based on SegNet, but with some modifications
rgb-footprint-extract -> a Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery, DeepLavV3+ module with a Dilated ResNet C42 backbone
SpaceNetExploration -> A sample project demonstrating how to extract building footprints from satellite images using a semantic segmentation model. Data from the SpaceNet Challenge
Rooftop-Instance-Segmentation -> VGG-16, Instance Segmentation, uses the Airs dataset
solar-farms-mapping -> An Artificial Intelligence Dataset for Solar Energy Locations in India
poultry-cafos -> This repo contains code for detecting poultry barns from high-resolution aerial imagery and an accompanying dataset of predicted barns over the United States
ssai-cnn -> This is an implementation of Volodymyr Mnih's dissertation methods on his Massachusetts road & building dataset
Remote-sensing-building-extraction-to-3D-model-using-Paddle-and-Grasshopper
segmentation-enhanced-resunet -> Urban building extraction in Daejeon region using Modified Residual U-Net (Modified ResUnet) and applying post-processing
Mask RCNN for Spacenet Off Nadir Building Detection
GRSL_BFE_MA -> Deep Learning-based Building Footprint Extraction with Missing Annotations using a novel loss function
FER-CNN -> Detection, Classification and Boundary Regularization of Buildings in Satellite Imagery Using Faster Edge Region Convolutional Neural Networks, with paper
UNET-Image-Segmentation-Satellite-Picture -> Unet to predict roof tops on Crowed AI Mapping dataset, uses keras
Vector-Map-Generation-from-Aerial-Imagery-using-Deep-Learning-GeoSpatial-UNET -> applied to geo-referenced images which are very large size > 10k x 10k pixels
building-footprint-segmentation -> pip installable library to train building footprint segmentation on satellite and aerial imagery, applied to Massachusetts Buildings Dataset and Inria Aerial Image Labeling Dataset
SemSegBuildings -> Project using fast.ai framework for semantic segmentation on Inria building segmentation dataset
FCNN-example -> overfit to a given single image to detect houses
SAT2LOD2 -> an open-source, python-based GUI-enabled software that takes the satellite images as inputs and returns LoD2 building models as outputs, with paper
SatFootprint -> building segmentation on the Spacenet 7 dataset
Building-Detection -> code for running a Raster Vision experiment to train a model to detect buildings from satellite imagery in three cities in Latin America
Multi-building-tracker -> code for paper: Multi-target building tracker for satellite images using deep learning
Boundary Enhancement Semantic Segmentation for Building Extraction
UNet_keras_for_RSimage -> keras code for binary semantic segmentation
Spacenet-Building-Detection -> uses keras
LGPNet-BCD -> code for 2021 paper: Building Change Detection for VHR Remote Sensing Images via Local-Global Pyramid Network and Cross-Task Transfer Learning Strategy
MTL_homoscedastic_SRB -> code for 2021 paper: A Multi-Task Deep Learning Framework for Building Footprint Segmentation
UNet_CNN -> UNet model to segment building coverage in Boston using Remote sensing data, uses keras
FDANet -> code for 2021 paper: Full-Level Domain Adaptation for Building Extraction in Very-High-Resolution Optical Remote-Sensing Images
CBRNet -> code for 2022 paper: A Coarse-to-fine Boundary Refinement Network for Building Extraction from Remote Sensing Imagery
ASLNet -> code for 2021 paper: Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images
BRRNet -> implementation of Modified U-Net from 2020 paper: BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images
Multi-Scale-Filtering-Building-Index -> Python implementation of building extraction index proposed in 2019 paper: A Multi - Scale Filtering Building Index for Building Extraction in Very High - Resolution Satellite Imagery
Models for Remote Sensing -> long list of unets etc applied to building detection
boundary_loss_for_remote_sensing -> code for 2019 paper: Boundary Loss for Remote Sensing Imagery Semantic Segmentation
Open Cities AI Challenge -> Segmenting Buildings for Disaster Resilience. Winning solutions on Github
MAPNet -> code for 2020 paper: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery
dual-hrnet -> localizing buildings and classifying their damage level
ESFNet -> code for 2019 paper: Efficient Network for Building Extraction from High-Resolution Aerial Images
rooftop-detection-python -> Detect Rooftops from low resolution satellite images and calculate area for cultivation and solar panel installment using classical computer vision techniques
keras_segmentation_models -> code for 2022 paper: Using Open Vector-Based Spatial Data to Create Semantic Datasets for Building Segmentation for Raster Data
CVCMFFNet -> code for 2021 paper: Complex-Valued Convolutional and Multifeature Fusion Network for Building Semantic Segmentation of InSAR Images
STEB-UNet -> code for 2022 paper: A Swin Transformer-Based Encoding Booster Integrated in U-Shaped Network for Building Extraction
dfc2020_baseline -> Baseline solution for the IEEE GRSS Data Fusion Contest 2020. Predict land cover labels from Sentinel-1 and Sentinel-2 imagery. Code for 2020 paper: Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping
Fusing multiple segmentation models based on different datasets into a single edge-deployable model -> roof, car & road segmentation
ground-truth-gan-segmentation -> use Pix2Pix to segment the footprint of a building. The dataset used is AIRS
UNICEF-Giga_Sudan -> Detecting school lots from satellite imagery in Southern Sudan using a UNET segmentation model
building_footprint_extraction -> The project retrieves satellite imagery from Google and performs building footprint extraction using a U-Net.
projectRegularization -> code for 2019 paper: Regularization of building boundaries in satellite images using adversarial and regularized losses
PolyWorldPretrainedNetwork -> code for 2021 paper: Polygonal Building Extraction with Graph Neural Networks in Satellite Images
dl_image_segmentation -> code for 2022 paper: Uncertainty-Aware Interpretable Deep Learning for Slum Mapping and Monitoring. Uses SHAP
UBC-dataset -> a dataset for building detection and classification from very high-resolution satellite imagery with the focus on object-level interpretation of individual buildings
GeoSeg -> code for 2022 paper: UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery
BESNet -> code for 2022 paper: BES-Net: Boundary Enhancing Semantic Context Network for High-Resolution Image Semantic Segmentation. Applied to Vaihingen and Potsdam datasets

Segmentation - Solar panels

DeepSolar -> A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. Dataset on kaggle, actually used a CNN for classification and segmentation is obtained by applying a threshold to the activation map. Original code is tf1 but tf2/kers and a pytorch implementation are available. Also checkout Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in .. Virginia
hyperion_solar_net -> trained classificaton & segmentation models on RGB imagery from Google Maps. Provides app for viewing predictions, and has arxiv paper and a nice webpage
3D-PV-Locator -> Large-scale detection of rooftop-mounted photovoltaic systems in 3D
PV_Pipeline -> PyTorch models and pipeline developed for "DeepSolar for Germany"
solar-panels-detection -> using SegNet, Fast SCNN & ResNet
predict_pv_yield -> Using optical flow & machine learning to predict PV yield
Large-scale-solar-plant-monitoring -> code for the paper "Remote Sensing for Monitoring of Photovoltaic Power Plants in Brazil Using Deep Semantic Segmentation"
Panel-Segmentation -> Determine the presence of a solar array in the satellite image (boolean True/False), using a VGG16 classification model
Roofpedia -> an open registry of green roofs and solar roofs across the globe identified by Roofpedia through deep learning
Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data Medium article, using 20cm imagery & Unet
solar-pv-global-inventory -> code from the Nature paper of Kruitwagen et al, used to produce a global inventory of utility-scale solar photvoltaic generating stations
remote-sensing-solar-pv -> A repository for sharing progress on the automated detection of solar PV arrays in sentinel-2 remote sensing imagery
solar-panel-segmentation) -> Finding solar panels using USGS satellite imagery
solar_seg -> Solar segmentation of PV modules (sub elements of panels) using drone images and fast.ai
solar_plant_detection -> boundary extraction of Photovoltaic (PV) plants using Mask RCNN and Amir dataset
SolarDetection -> unet on satellite image from the USA and France
adopptrs -> Automatic Detection Of Photovoltaic Panels Through Remote Sensing using unet & pytorch
solar-panel-locator -> the number of solar panel pixels was only ~0.2% of the total pixels in the dataset, so solar panel data was upsampled to account for the class imbalance
projects-solar-panel-detection -> List of project to detect solar panels from aerial/satellite images
Satellite_ComputerVision -> UNET to detect solar arrays from Sentinel-2 data, using Google Earth Engine and Tensorflow. Also covers parking lot detection

Segmentation - Electrical substations

The repos below resulted from the ICETCI 2021 competition on Machine Learning based feature extraction of Electrical Substations from Satellite Data using Open Source Tools

Aarsh2001/ML_Challenge_NRSC -> 3rd place entry
Electrical-Substation-detection-by-fine-tunning-Segnet-Architecture -> 9th place entry
electrical_substation_detection -> entry using UNet, Albumentations for image augmentation, and OpenCV for computer vision tasks

Instance segmentation

In instance segmentation, each individual 'instance' of a segmented area is given a unique lable. For detection of very small objects this may a good approach, but it can struggle seperating individual objects that are closely spaced.

Mask_RCNN generates bounding boxes and segmentation masks for each instance of an object in the image. It is very commonly used for instance segmentation & object detection
Instance segmentation of center pivot irrigation system in Brazil using free Landsat images, mask R-CNN & Keras
Oil tank instance segmentation with Mask R-CNN with accompanying article using Keras & Airbus Oil Storage Detection Dataset on Kaggle
Building-Detection-MaskRCNN -> Building detection from the SpaceNet dataset by using Mask RCNN
Mask_RCNN-for-Caravans -> detect caravan footprints from OS imagery
parking_bays_detectron2 -> Detecting parking bays with satellite imagery. Used Detectron2 and synthetic data with Unreal, superior performance to using Mask RCNN
Locate buildings with a dark roof that feed heat island phenomenon using Mask RCNN -> with repo, used INRIA dataset & labelme for annotation
Circle_Finder -> Circular Shapes Detection in Satellite Imagery, 2nd place solution to the Circle Finder Challenge
Lawn_maskRCNN -> Detecting lawns from satellite images of properties in the Cedar Rapids area using Mask-R-CNN
CropMask_RCNN -> Segmenting center pivot agriculture to monitor crop water use in drylands with Mask R-CNN and Landsat satellite imagery
Mask RCNN for Spacenet Off Nadir Building Detection
CATNet -> code for 2021 paper: Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images
Object-Detection-on-Satellite-Images-using-Mask-R-CNN -> detect ships
FactSeg -> Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery (TGRS), also see FarSeg and FreeNet, implementations of research paper
aqua_python -> detecting aquaculture farms using Mask R-CNN

Panoptic segmentation

Things and stuff or how remote sensing could benefit from panoptic segmentation
Panoptic Segmentation Meets Remote Sensing (paper)
pastis-benchmark
Panoptic-Generator -> This module converts GIS data into panoptic segmentation tiles
BSB-Aerial-Dataset -> an example on how to use Detectron2's Panoptic-FPN in the BSB Aerial Dataset
utae-paps -> PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation

Object detection

Several different techniques can be used to count the number of objects in an image. The returned data can be an object count (regression), a bounding box around individual objects in an image (typically using Yolo or Faster R-CNN architectures), a pixel mask for each object (instance segmentation), key points for an an object (such as wing tips, nose and tail of an aircraft), or simply a classification for a sliding tile over an image. A good introduction to the challenge of performing object detection on aerial imagery is given in this paper. In summary, images are large and objects may comprise only a few pixels, easily confused with random features in background. For the same reason, object detection datasets are inherently imbalanced, since the area of background typically dominates over the area of the objects to be detected. In general object detection performs well on large objects, and gets increasingly difficult as the objects get smaller & more densely packed. Model accuracy falls off rapidly as image resolution degrades, so it is common for object detection to use very high resolution imagery, e.g. 30cm RGB. A particular characteristic of aerial images is that objects can be oriented in any direction, so using rotated bounding boxes which aligning with the object can be crucial for extracting metrics such as the length and width of an object. Note that newer models such as Yolov5 may not achieve the same performance as 'older' models like Faster RCNN or Retinanet since they are no longer pre-trained on datasets such as ImageNet, but smaller datasets such as COCO dataset.

Background reading: Anchor Boxes for Object Detection
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review
awesome-aerial-object-detection bu murari023, another by visionxiang and awesome-tiny-object-detection list many relevant papers
Tackling the Small Object Problem in Object Detection
Object Detection Accuracy as a Function of Image Resolution -> Medium article using COWC dataset, performance rapidly degrades below 30cm imagery
Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) -> combines some of the leading object detection algorithms into a unified framework designed to detect objects both large and small in overhead imagery. Train models and test on arbitrary image sizes with YOLO (versions 2 and 3), Faster R-CNN, SSD, or R-FCN.
YOLTv4 -> YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitrarily large images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks. Read Announcing YOLTv4: Improved Satellite Imagery Object Detection
Tensorflow Benchmarks for Object Detection in Aerial Images -> tensorflow-based codebase created to build benchmarks for object detection in aerial images
Pytorch Benchmarks for Object Detection in Aerial Images -> pytorch-based codebase created to build benchmarks for object detection in aerial images using mmdetection
ASPDNet -> Counting dense objects in remote sensing images, arxiv paper
xview-yolov3 -> xView 2018 Object Detection Challenge: YOLOv3 Training and Inference
Faster RCNN for xView satellite data challenge
How to detect small objects in (very) large images -> A practical guide to using Slicing-Aided Hyper Inference (SAHI) for performing inference on the DOTAv1.0 object detection dataset using the mmdetection framework
Object detection on Satellite Imagery using RetinaNet -> using the Kaggle Swimming Pool and Car Detection dataset
Object Detection Satellite Imagery Multi-vehicles Dataset (SIMD) -> RetinaNet,Yolov3 and Faster RCNN for multi object detection on satellite images dataset
SNIPER/AutoFocus -> an efficient multi-scale object detection training/inference algorithm
marine_debris_ML -> Marine debris detection, uses 3-meter imagery product called Planetscope with bands in the red, green, blue, and near-infrared. Uses Tensorflow Object Detection API with pre-trained resnet 101
pool-detection-from-aerial-imagery -> Use Icevision and Detectron2 to detect swimming pools from aerial imagery
Electric-Pylon-Detection-in-RSI -> a dataset which contains 1500 remote sensing images of electric pylons used to train ten deep learning models
Synthesizing Robustness YOLTv4 Results Part 2: Dataset Size Requirements and Geographic Insights -> quantify how much harder rare objects are to localize
IS-Count -> IS-Count is a sampling-based and learnable method for estimating the total object count in a region.
Object Detection On Aerial Imagery Using RetinaNet -> ESRI Data Science Challenge 2019 3rd place solution
Clustered-Object-Detection-in-Aerial-Image
yolov5s_for_satellite_imagery -> yolov5s applied to the DOTA dataset
RetinaNet-PyTorch -> RetinaNet implementation on remote sensing ship dataset (SSDD)
Detecting-Cyclone-Centers-Custom-YOLOv3 -> tropical cyclones (TCs) are intense warm-corded cyclonic vortices, developed from low-pressure systems over the tropical oceans and driven by complex air-sea interaction
Object-Detection-YoloV3-RetinaNet-FasterRCNN -> trained on a private datset
Google-earth-Object-Recognition -> Code for training and evaluating on Dior Dataset (Google Earth Images) using RetinaNet and YOLOV5
AI-TOD -> a dataset for tiny object detection in aerial images. The mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than other datasets
HIECTOR: Hierarchical object detector at scale -> HIECTOR facilitates multiple satellite data collections of increasingly detailed spatial resolution for a cost-efficient and accurate object detection over large areas
Detection of Multiclass Objects in Optical Remote Sensing Images -> code for 2018 paper: Detection of Multiclass Objects in Optical Remote Sensing Images
SB-MSN -> Sampling-Balance based Multi-stage Network (SB-MSN) for aerial image object detection. Code for 2021 paper: Improving Training Instance Quality in Aerial Image Object Detection With a Sampling-Balance-Based Multistage Network
yoltv5 -> detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks. Uses YOLOv5 & pytorch
AIR -> A deep learning object detector framework written in Python for supporting Land Search and Rescue Missions
dior_detect -> benchmarks for object detection on DIOR dataset
Panchromatic to Multispectral: Object Detection Performance as a Function of Imaging Bands -> Medium article, concludes that more bands are not always beneficial, but likely varies by use case
OPLD-Pytorch -> code for 2020 paper: Learning Point-Guided Localization for Detection in Remote Sensing Images
F3Net -> code for 2020 paper: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images
GLNet -> code for 2021 paper: Global to Local: Clip-LSTM-Based Object Detection From Remote Sensing Images
SRAF-Net -> code for 2021 paper: A Scene-Relevant Anchor-Free Object Detection Network in Remote Sensing Images
object_detection_in_remote_sensing_images -> using CNN and attention mechanism
SHAPObjectDetection -> code for 2022 paper: SHAP-Based Interpretable Object Detection Method for Satellite Imagery
NWD -> code for 2021 paper: A Normalized Gaussian Wasserstein Distance for Tiny Object Detection. Uses AI-TOD dataset
MSFC-Net -> code for 2021 paper: Multiscale Semantic Fusion-Guided Fractal Convolutional Object Detection Network for Optical Remote Sensing Imagery

Object counting

When the object count, but not its shape is required, U-net can be used to treat this as an image-to-image translation problem.

centroid-unet -> Centroid-UNet is deep neural network model to detect centroids from satellite images, with paper
count-sea-lion -> uses keras & Count-ception network
cownter_strike -> counting cows, located with point-annotations, two models: CSRNet (a density-based method) & LCFCN (a detection-based method)
DO-U-Net -> an effective approach for when the size of an object needs to be known, as well as the number of objects in the image, initially created to segment and count Internally Displaced People (IDP) camps in Afghanistan
Cassava Crop Counting
Counting from Sky -> A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method
PSGCNet -> code for 2022 paper: PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images

Object detection with rotated bounding boxes

OBB: orinted bounding boxes are polygons representing rotated rectangles
For datasets checkout DOTA & HRSC2016
mmrotate -> Rotated Object Detection Benchmark, with pretrained models and function for inferencing on very large images
OBBDetection -> an oriented object detection library, which is based on MMdetection
rotate-yolov3 -> Rotation object detection implemented with yolov3. Also see yolov3-polygon
DRBox -> for detection tasks where the objects are orientated arbitrarily, e.g. vehicles, ships and airplanes
s2anet -> Official code of the paper 'Align Deep Features for Oriented Object Detection'
CFC-Net -> Official implementation of "CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images"
ReDet -> Official code of the paper "ReDet: A Rotation-equivariant Detector for Aerial Object Detection"
BBAVectors-Oriented-Object-Detection -> Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors
CSL_RetinaNet_Tensorflow -> Code for ECCV 2020 paper: Arbitrary-Oriented Object Detection with Circular Smooth Label
r3det-on-mmdetection -> R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
R-DFPN_FPN_Tensorflow -> Rotation Dense Feature Pyramid Networks (Tensorflow)
R2CNN_Faster-RCNN_Tensorflow -> Rotational region detection based on Faster-RCNN
Rotated-RetinaNet -> implemented in pytorch, it supports the following datasets: DOTA, HRSC2016, ICDAR2013, ICDAR2015, UCAS-AOD, NWPU VHR-10, VOC2007
OBBDet_Swin -> The sixth place winning solution in 2021 Gaofen Challenge
CG-Net -> Learning Calibrated-Guidance for Object Detection in Aerial Images
OrientedRepPoints_DOTA -> Oriented RepPoints + Swin Transformer/ReResNet
yolov5_obb -> yolov5 + Oriented Object Detection
How to Train YOLOv5 OBB -> YOLOv5 OBB tutorial and YOLOv5 OBB noteboook
OHDet_Tensorflow -> can be applied to rotation detection and object heading detection
Seodore -> framework maintaining recent updates of mmdetection
Rotation-RetinaNet-PyTorch -> oriented detector Rotation-RetinaNet implementation on Optical and SAR ship dataset
AIDet -> an open source object detection in aerial image toolbox based on MMDetection
rotation-yolov5 -> rotation detection based on yolov5
ShipDetection -> Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box, based on Faster R-CNN and ORN, uses caffe
SLRDet -> project based on mmdetection to reimplement RRPN and use the model Faster R-CNN OBB
AxisLearning -> code for 2020 paper: Axis Learning for Orientated Objects Detection in Aerial Images
Detection_and_Recognition_in_Remote_Sensing_Image -> This work uses PaNet to realize Detection and Recognition in Remote Sensing Image by MXNet
DrBox-v2-tensorflow -> tensorflow implementation of DrBox-v2 which is an improved detector with rotatable boxes for target detection in remote sensing images
Rotation-EfficientDet-D0 -> A PyTorch Implementation Rotation Detector based EfficientDet Detector, applied to custom rotation vehicle datasets
DODet -> Dual alignment for oriented object detection, uses DOTA dataset
GF-CSL -> code for 2022 paper: Gaussian Focal Loss: Learning Distribution Polarized Angle Prediction for Rotated Object Detection in Aerial Images
simplified_rbox_cnn -> code for 2018 paper: RBox-CNN: rotated bounding box based CNN for ship detection in remote sensing image. Uses Tensorflow object detection API
Polar-Encodings -> code for 2021 [paper](Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images)
R-CenterNet -> detector for rotated-object based on CenterNet
piou -> Orientated Object Detection; IoU Loss, applied to DOTA dataset
DAFNe -> code for 2021 paper: DAFNe: A One-Stage Anchor-Free Approach for Oriented Object Detection
AProNet -> code for 2021 paper: AProNet: Detecting objects with precise orientation from aerial images. Applied to datasets DOTA and HRSC2016
UCAS-AOD-benchmark -> A benchmark of UCAS-AOD dataset
RotateObjectDetection -> based on Ultralytics/yolov5, with adjustments to enable rotate prediction boxes. Also see PolygonObjectDetection
AD-Toolbox -> Aerial Detection Toolbox based on MMDetection and MMRotate, with support for more datasets
GGHL -> code for 2022 paper: A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection

Object detection enhanced by super resolution

Super-Resolution and Object Detection -> Super-resolution is a relatively inexpensive enhancement that can improve object detection performance
EESRGAN -> Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network
Mid-Low Resolution Remote Sensing Ship Detection Using Super-Resolved Feature Representation
EESRGAN -> code for 2020 paper: Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network. Applied to COWC & OGST datasets
FBNet -> code for 2022 paper: Feature Balance for Fine-Grained Object Classification in Aerial Images

Salient object detection

Detecting the most noticeable or important object in a scene

ACCoNet -> Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images
MCCNet -> Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images
CorrNet -> Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation
Reading list for deep learning based Salient Object Detection in Optical Remote Sensing Images
ORSSD-dataset -> salient object detection dataset
EORSSD-dataset -> Extended Optical Remote Sensing Saliency Detection (EORSSD) Dataset
DAFNet_TIP20 -> code for 2020 paper: Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
EMFINet -> code for 2021 paper: Edge-Aware Multiscale Feature Integration Network for Salient Object Detection in Optical Remote Sensing Images
ERPNet -> code for 2022 paper: Edge-guided Recurrent Positioning Network for Salient Object Detection in Optical Remote Sensing Images
FSMINet -> code for 2022 paper: Fully Squeezed Multi-Scale Inference Network for Fast and Accurate Saliency Detection in Optical Remote Sensing Images
AGNet -> code for 2022 paper: AGNet: Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images
MSCNet -> code for 2022 paper: A lightweight multi-scale context network for salient object detection in optical remote sensing images

Object detection - buildings, rooftops & solar panels

Machine Learning For Rooftop Detection and Solar Panel Installment discusses tiling large images and generating annotations from OSM data. Features of the roofs were calculated using a combination of contour detection and classification. Follow up article using semantic segmentation
Building Extraction with YOLT2 and SpaceNet Data
AIcrowd dataset of building outlines -> 300x300 pixel RGB images with annotations in MS-COCO format
XBD-hurricanes -> Models for building (and building damage) detection in high-resolution (<1m) satellite and aerial imagery using a modified RetinaNet model
Detecting solar panels from satellite imagery using segmentation
ssd-spacenet -> Detect buildings in the Spacenet dataset using Single Shot MultiBox Detector (SSD)
3DBuildingInfoMap -> simultaneous extraction of building height and footprint from Sentinel imagery using ResNet
Solar Panel Detection -> using Faster R-CNN & Tensorflow object detection API. With repo
DeepSolaris -> a EuroStat project to detect solar panels in aerial images, further material here
ML_ObjectDetection_CAFO -> Detect Concentrated Animal Feeding Operations (CAFO) in Satellite Imagery
Multi-level-Building-Detection-Framework -> code for 2018 paper: Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks
satellite_image_tinhouse_detector -> Detection of tin houses from satellite/aerial images using the Tensorflow Object Detection API
Automatic Damage Annotation on Post-Hurricane Satellite Imagery -> detect damaged buildings using tensorflow object detection API. With repos here and here
mappingchallenge -> YOLOv5 applied to the AICrowd Mapping Challenge dataset

Object detection - ships & boats

Also see the kaggle competition run by Airbus
How hard is it for an AI to detect ships on satellite images?
Object Detection in Satellite Imagery, a Low Overhead Approach
Detecting Ships in Satellite Imagery using the Planet dataset and Keras
Planet use non DL felzenszwalb algorithm to detect ships
Ship detection using k-means clustering & CNN classifier on patches
sentinel2-xcube-boat-detection -> detect and count boat traffic in Sentinel-2 imagery using temporal, spectral and spatial features
Arbitrary-Oriented Ship Detection through Center-Head Point Extraction -> arxiv paper. Keypoint estimation is performed to find the center of ships. Then, the size and head point of the ships are regressed. Repo ASD
ship_detection -> using an interesting combination of CNN classifier, Class Activation Mapping (CAM) & UNET segmentation. Accompanying three part blog post
Building a complete Ship detection algorithm using YOLOv3 and Planet satellite images -> covers finding and annotating data (using LabelMe), preprocessing large images into chips, and training Yolov3. Repo
Ship-detection-in-satellite-images -> experiments with UNET, YOLO, Mask R-CNN, SSD, Faster R-CNN, RETINA-NET
Ship-Detection-from-Satellite-Images-using-YOLOV4 -> uses Kaggle Airbus Ship Detection dataset
kaggle-airbus-ship-detection-challenge -> using oriented SSD
shipsnet-detector -> Detect container ships in Planet imagery using machine learning
Classifying Ships in Satellite Imagery with Neural Networks -> applied to the Kaggle Ships in Satellite Imagery dataset
Mask R-CNN for Ship Detection & Segmentation blog post with repo
contrastive_SSL_ship_detection -> Contrastive self supervised learning for ship detection in Sentinel 2 images
Boat detection with multi-region-growing method in satellite images
small-boat-detector -> Trained yolo v3 model weights and configuration file to detect small boats in satellite imagery
Satellite-Imagery-Datasets-Containing-Ships -> A list of optical and radar satellite datasets for ship detection, classification, semantic segmentation and instance segmentation tasks
Ship-Classification-in-Satellite-Images -> Convolutional neural network model for ship classification in satellite images
Ship-Detection -> CNN approach for ship detection in the ocean using a satellite image
vesselTracker -> Project based on reduced model of Yolov5 architecture using Pytorch. Custom dataset based on SAR imagery provided by Sentinel-1 through Earth Engine API
marine-debris-ml-model -> Marine Debris Detection using tensorflow object detection API
SDGH-Net -> code for 2021 paper: SDGH-Net: Ship Detection in Optical Remote Sensing Images Based on Gaussian Heatmap Regression
LR-TSDet -> code for 2021 paper: LR-TSDet: Towards Tiny Ship Detection in Low-Resolution Remote Sensing Images
FGSCR-42 -> A public Dataset for Fine-Grained Ship Classification in Remote sensing images
ShipDetection -> Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box, based on Faster R-CNN and ORN, uses caffe
WakeNet -> A CNN-based optical image ship wake detector, code for 2021 paper: Rethinking Automatic Ship Wake Detection: State-of-the-Art CNN-based Wake Detection via Optical Images
Histogram of Oriented Gradients (HOG) Boat Heading Classification -> Medium article
Object Detection in Satellite Imagery, a Low Overhead Approach -> Medium article which demonstrates how to combine Canny edge detector pre-filters with HOG feature descriptors, random forest classifiers, and sliding windows to perform ship detection
simplified_rbox_cnn -> code for 2018 paper: RBox-CNN: rotated bounding box based CNN for ship detection in remote sensing image. Uses Tensorflow object detection API
Ship-Detection-based-on-YOLOv3-and-KV260 -> entry project of the Xilinx Adaptive Computing Challenge 2021. It uses YOLOv3 for ship target detection in optical remote sensing images, and deploys DPU on the KV260 platform to achieve hardware acceleration
LEVIR-Ship -> a dataset for tiny ship detection under medium-resolution remote sensing images
Push-and-Pull-Network -> code for 2022 paper: Contrastive Learning for Fine-grained Ship Classification in Remote Sensing Images

Object detection - cars, vehicles & trains

Truck Detection with Sentinel-2 during COVID-19 crisis -> moving objects in Sentinel-2 data causes a specific reflectance relationship in the RGB, which looks like a rainbow, and serves as a marker for trucks. Improve accuracy by only analysing roads. Not using object detection but relevant. Also see S2TD
cowc_car_counting -> car counting on the Cars Overhead With Context (COWC) dataset. Not sctictly object detection but a CNN to predict the car count in a tile
CarCounting -> using Yolov3 & COWC dataset
Traffic density estimation as a regression problem instead of object detection -> inspired by this paper
Applying Computer Vision to Railcar Detection -> useful insights into counting railcars (i.e. train carriages) using Mask-RCNN with rotated bounding boxes output
Leveraging Deep Learning for Vehicle Detection And Classification
Detection of parkinglots and driveways with retinanet
pytorch-vedai -> object detection on the VEDAI dataset: Vehicle Detection in Aerial Imagery
Rotation-EfficientDet-D0 -> PyTorch implementation of Rotated EfficientDet, applied to a custom rotation vehicle dataset (car counting)
RSVC2021-Dataset -> A dataset for Vehicle Counting in Remote Sensing images, created from the DOTA & ITCVD Datasets
Car Localization and Counting with Overhead Imagery, an Interactive Exploration -> Medium article by Adam Van Etten

Object detection - planes & aircraft

yoltv4 includes examples on the RarePlanes dataset
aircraft-detection -> experiments to test the performance of a Gaussian process (GP) classifier with various kernels on the UC Merced land use land cover (LULC) dataset
The Rareplanes guide recommends annotating airplanes in a diamond style, which has several advantages (easily reproducible, convertible to a bounding box etc) and allows extracting the aircraft length and wingspan
rareplanes-yolov5 -> using YOLOv5 and the RarePlanes dataset to detect and classify sub-characteristics of aircraft
detecting-aircrafts-on-airbus-pleiades-imagery-with-yolov5
Using Detectron2 to segment aircraft from satellite imagery -> pytorch and Rare Planes
Faster RCNN to detect airplanes
aircraft-detection-from-satellite-images-yolov3 -> trained on kaggle cgi-planes-in-satellite-imagery-w-bboxes dataset
HRPlanesv2-Data-Set -> YOLOv4 and YOLOv5 weights trained on the HRPlanesv2 dataset
Deep-Learning-for-Aircraft-Recognition -> A CNN model trained to classify and identify various military aircraft through satellite imagery
FRCNN-for-Aircraft-Detection -> faster-rcnn & keras
ergo-planes-detector -> An ergo based project that relies on a convolutional neural network to detect airplanes from satellite imagery, uses the PlanesNet dataset
pytorch-remote-sensing -> Aircraft detection using the 'Airbus Aircraft Detection' dataset and Faster-RCNN with ResNet-50 backbone using pytorch
FasterRCNN_ObjectDetection -> faster RCNN model for aircraft detection and localisation in satellite images and creating a webpage with live server for public usage

Object detection - infrastructure & utilities

wind-turbine-detector -> Wind Turbine Object Detection from Aerial Imagery Using TensorFlow Object Detection API
Water Tanks and Swimming Pools Detection -> uses Faster R-CNN
PCAN -> Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery, with dataset

Object detection - animals

A variety of techniques can be used to count animals, including object detection and instance segmentation. For convenience they are all listed here:

cownter_strike -> counting cows, located with point-annotations, two models: CSRNet (a density-based method) & LCFCN (a detection-based method)
elephant_detection -> Using Keras-Retinanet to detect elephants from aerial images
CNN-Mosquito-Detection -> determining the locations of potentially dangerous breeding grounds, compared YOLOv4, YOLOR & YOLOv5
Borowicz_etal_Spacewhale -> locate whales using ResNet
walrus-detection-and-count -> uses Mask R-CNN instance segmentation

Object tracking in videos

Object Tracking in Satellite Videos Based on a Multi-Frame Optical Flow Tracker arxiv paper
CFME -> Object Tracking in Satellite Videos by Improved Correlation Filters With Motion Estimations
TGraM -> code and dataset for 2022 paper: Multi-Object Tracking in Satellite Videos with Graph-Based Multi-Task Modeling
satellite_video_mod_groundtruth -> groundtruth on satellite video for evaluating moving object detection algorithm
Moving-object-detection-DSFNet -> code for 2021 paper: DSFNet: Dynamic and Static Fusion Network for Moving Object Detection in Satellite Videos
HiFT -> code for 2021 paper: HiFT: Hierarchical Feature Transformer for Aerial Tracking
TCTrack -> code for 2022 paper: TCTrack: Temporal Contexts for Aerial Tracking

Counting trees

DeepForest is a python package for training and predicting individual tree crowns from airborne RGB imagery
Official repository for the "Identifying trees on satellite images" challenge from Omdena
Counting-Trees-using-Satellite-Images -> create an inventory of incoming and outgoing trees for an annual tree inspections, uses keras & semantic segmentation
2020 Nature paper - An unexpectedly large count of trees in the West African Sahara and Sahel -> tree detection framework based on U-Net & tensorflow 2 with code here
TreeDetection -> A color-based classifier to detect the trees in google image data along with tree visual localization and crown size calculations via OpenCV

Oil storage tank detection & oil spills

Oil is stored in tanks at many points between extraction and sale, and the volume of oil in storage is an important economic indicator.

A Beginner’s Guide To Calculating Oil Storage Tank Occupancy With Help Of Satellite Imagery
Oil Storage Tank’s Volume Occupancy On Satellite Imagery Using YoloV3 with repo
Oil-Tank-Volume-Estimation -> combines object detection and classical computer vision
MCAN-OilSpillDetection -> Oil Spill Detection with A Multiscale Conditional Adversarial Network under Small Data Training, with paper. A multiscale conditional adversarial network (MCAN) trained with four oil spill observation images accurately detects oil spills in new images.
Oil tank instance segmentation with Mask R-CNN with accompanying article using Keras & Airbus Oil Storage Detection Dataset on Kaggle
https://www.kaggle.com/towardsentropy/oil-storage-tanks -> large kaggle dataset, note however that approx 85% of images contain no tanks
https://www.kaggle.com/airbusgeo/airbus-oil-storage-detection-dataset -> smaller kaggle dataset
ognet -> a Global Oil and Gas Infrastructure Database using Deep Learning on Remotely Sensed Imagery
RSOD-Dataset -> dataset for object detection in PASCAL VOC format. Aircraft, playgrounds, overpasses & oiltanks
oil_storage-detector -> using yolov5 and the Airbus Oil Storage Detection dataset
oil_well_detector -> detect oil wells in the Bakken oil field based on satellite imagery
OGST -> Oil and Gas Tank Dataset
AContrarioTankDetection -> code for 2020 paper: Oil Tank Detection in Satellite Images via a Contrario Clustering
SubpixelCircleDetection -> code for 2020 paper: CIRCULAR-SHAPED OBJECT DETECTION IN LOW RESOLUTION SATELLITE IMAGES
Oil Storage Detection on Airbus Imagery with YOLOX -> uses the Kaggle Airbus Oil Storage Detection dataset

Cloud detection & removal

Generally treated as a semantic segmentation problem or custom features created using band math

See section Kaggle - Understanding Clouds from Satellite Images
From this article on sentinelhub there are three popular classical algorithms that detects thresholds in multiple bands in order to identify clouds. In the same article they propose using semantic segmentation combined with a CNN for a cloud classifier (excellent review paper here), but state that this requires too much compute resources.
This article compares a number of ML algorithms, random forests, stochastic gradient descent, support vector machines, Bayesian method.
Segmentation of Clouds in Satellite Images Using Deep Learning -> semantic segmentation using a Unet on the Kaggle 38-Cloud dataset
Cloud Detection in Satellite Imagery compares FPN+ResNet18 and CheapLab architectures on Sentinel-2 L1C and L2A imagery
Cloud-Removal-with-GAN-Satellite-Image-Processing
Benchmarking Deep Learning models for Cloud Detection in Landsat-8 and Sentinel-2 images
Landsat-8 to Proba-V Transfer Learning and Domain Adaptation for Cloud detection
Multitemporal Cloud Masking in Google Earth Engine
s2cloudmask -> Sentinel-2 Cloud and Shadow Detection using Machine Learning
sentinel2-cloud-detector -> Sentinel Hub Cloud Detector for Sentinel-2 images in Python
dsen2-cr -> cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion, contains the model code, written in Python/Keras, as well as links to pre-trained checkpoints and the SEN12MS-CR dataset
pyatsa -> Python package implementing the Automated Time-Series Analysis method for masking clouds in satellite imagery developed by Zhu and Helmer 2018
decloud -> Decloud enables the training of various deep nets to remove clouds in optical image, using e.g. Sentinel 1 & 2
cloudless -> Deep learning pipeline for orbital satellite data for detecting clouds
Deep-Gapfill -> Official implementation of Optical image gap filling using deep convolutional autoencoder from optical and radar images
satellite-cloud-removal-dip -> Satellite cloud removal with Deep Image Prior, with paper
cloudFCN -> Python 3 package for Fully Convolutional Network development, specifically for cloud masking
Fmask -> Fmask (Function of mask) is used for automated clouds, cloud shadows, snow, and water masking for Landsats 4-9 and Sentinel 2 images, in Matlab. Also see PyFmask
HOW TO USE DEEP LEARNING, PYTORCH LIGHTNING, AND THE PLANETARY COMPUTER TO PREDICT CLOUD COVER IN SATELLITE IMAGERY
cloud-cover-winners -> Code from the winning submissions for the On Cloud N: Cloud Cover Detection Challenge
On-Cloud-N: Cloud Cover Detection Challenge - 19th Place Solution
ukis-csmask -> package to masks clouds in Sentinel-2, Landsat-8, Landsat-7 and Landsat-5 images
OpenSICDR -> long list of satellite image cloud detection resources
RS-Net -> code for the paper: A cloud detection algorithm for satellite imagery based on deep learning
Clouds-Segmentation-Project -> treats as a 3 class problem; Open clouds, Closed clouds and no clouds, uses pytorch on a dataset that consists of IR & Visual Grayscale images
STGAN -> PyTorch Implementation of STGAN for Cloud Removal in Satellite Images, with paper
mcgan-cvprw2017-pytorch -> code for 2017 paper: Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
Cloud-Net: A semantic segmentation CNN for cloud detection -> an end-to-end cloud detection algorithm for Landsat 8 imagery, trained on 38-Cloud Training Set
fcd -> code for 2021 paper: Fixed-Point GAN for Cloud Detection. A weakly-supervised approach, training with only image-level labels
CloudX-Net -> an efficient and robust architecture used for detection of clouds from satellite images
A simple cloud-detection walk-through using Convolutional Neural Network (CNN and U-Net) and fast.ai library
38Cloud-Medium -> Walk-through using u-net to detect clouds in satellite images with fast.ai
cloud_detection_using_satellite_data -> performed on Sentinel 2 data
Luojia1-Cloud-Detection -> Luojia-1 Satellite Visible Band Nighttime Imagery Cloud Detection
SEN12MS-CR-TS -> code for 2022 paper: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal
ES-CCGAN -> This is a dehazed method for remote sensing image, which based on CycleGAN
Cloud_Classification_DL -> Classifying cloud organization patterns from satellite images using Deep Learning techniques (Mask R-CNN)
CNN-based-Cloud-Detection-Methods -> Understanding the Role of Receptive Field of Convolutional Neural Network for Cloud Detection in Landsat 8 OLI Imagery
cloud-removal-deploy -> flask app for cloud removal
CloudMattingGAN -> code for 2019 paper: Generative Adversarial Training for Weakly Supervised Cloud Matting
atrain-cloudseg -> Official repository for the A-Train Cloud Segmentation Dataset
CDnet -> code for 2019 paper: CNN-Based Cloud Detection for Remote Sensing Imager
GLNET -> code for 2021 paper: Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments
CDnetV2 -> code for 2021 paper: CNN-Based Cloud Detection for Remote Sensing Imagery With Cloud-Snow Coexistence
grouped-features-alignment -> code for 2021 paper: Unsupervised Domain Adaptation for Cloud Detection Based on Grouped Features Alignment and Entropy Minimization
Detecting Cloud Cover Via Sentinel-2 Satellite Data -> blog post on Benjamin Warners Top-10 Percent Solution to DrivenData’s On CloudN Competition using fast.ai & customized version of XResNeXt50. Repo
AISD -> code (Matlab) and dataset for 2020 paper: Deeply supervised convolutional neural network for shadow detection based on a novel aerial shadow imagery dataset
CloudGAN -> Detecting and Removing Clouds from RGB-images using Image Inpainting
Using GANs to Augment Data for Cloud Image Segmentation Task -> code for 2021 paper
Cloud-Segmentation-from-Satellite-Imagery -> applied to Sentinel-2 dataset
HRC_WHU -> High-Resolution Cloud Detection Dataset comprising 150 RGB images and a resolution varying from 0.5 to 15 m in different global regions

Change detection

Generally speaking, change detection methods are applied to a pair of images to generate a mask of change, e.g. of buildings damaged in a disaster. Note, clouds & shadows change often too..!

awesome-remote-sensing-change-detection lists many datasets and publications
Change-Detection-Review -> A review of change detection methods, including code and open data sets for deep learning
Unstructured-change-detection-using-CNN
Siamese neural network to detect changes in aerial images -> uses Keras and VGG16 architecture
Change Detection in 3D: Generating Digital Elevation Models from Dove Imagery
QGIS plugin for applying change detection algorithms on high resolution satellite imagery
LamboiseNet -> Master thesis about change detection in satellite imagery using Deep Learning
Fully Convolutional Siamese Networks for Change Detection -> with paper
Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks -> with paper, used the Onera Satellite Change Detection (OSCD) dataset
STANet -> official implementation of the spatial-temporal attention neural network (STANet) for remote sensing image change detection
BIT_CD -> Official Pytorch Implementation of Remote Sensing Image Change Detection with Transformers
IAug_CDNet -> Official Pytorch Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images
dpm-rnn-public -> Code implementing a damage mapping method combining satellite data with deep learning
SenseEarth2020-ChangeDetection -> 1st place solution to the Satellite Image Change Detection Challenge hosted by SenseTime; predictions of five HRNet-based segmentation models are ensembled, serving as pseudo labels of unchanged areas
KPCAMNet -> Python implementation of the paper Unsupervised Change Detection in Multi-temporal VHR Images Based on Deep Kernel PCA Convolutional Mapping Network
CDLab -> benchmarking deep learning-based change detection methods.
Siam-NestedUNet -> The pytorch implementation for "SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images"
SUNet-change_detection -> Implementation of paper SUNet: Change Detection for Heterogeneous Remote Sensing Images from Satellite and UAV Using a Dual-Channel Fully Convolution Network
Self-supervised Change Detection in Multi-view Remote Sensing Images
MFPNet -> Remote Sensing Change Detection Based on Multidirectional Adaptive Feature Fusion and Perceptual Similarity
GitHub for the DIUx xView Detection Challenge -> The xView2 Challenge focuses on automating the process of assessing building damage after a natural disaster
DASNet -> Dual attentive fully convolutional siamese networks for change detection of high-resolution satellite images
Self-Attention for Raw Optical Satellite Time Series Classification
planet-movement -> Find and process Planet image pairs to highlight object movement
UNet-based-Unsupervised-Change-Detection -> A convolutional neural network (CNN) and semantic segmentation is implemented to detect the changes between the images, as well as classify the changes into the correct semantic class, with arxiv paper
temporal-cluster-matching -> detecting change in structure footprints from time series of remotely sensed imagery
autoRIFT -> fast and intelligent algorithm for finding the pixel displacement between two images
DSAMNet -> Code for “A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection”. The main types of changes in the dataset include: (a) newly built urban buildings; (b) suburban dilation; (c) groundwork before construction; (d) change of vegetation; (e) road expansion; (f) sea construction.
SRCDNet -> The pytorch implementation for "Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions ". SRCDNet is designed to learn and predict change maps from bi-temporal images with different resolutions
Land-Cover-Analysis -> Land Cover Change Detection using Satellite Image Segmentation
A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images
Satellite-Image-Alignment-Differencing-and-Segmentation -> thesis on change detection
Change Detection in Multi-temporal Satellite Images -> uses Principal Component Analysis (PCA) and K-means clustering
Unsupervised Change Detection Algorithm using PCA and K-Means Clustering -> in Matlab but has paper
ChangeFormer -> A Transformer-Based Siamese Network for Change Detection. Uses transformer architecture to address the limitations of CNN in handling multi-scale long-range details. Demonstrates that ChangeFormer captures much finer details compared to the other SOTA methods, achieving better performance on benchmark datasets
Heterogeneous_CD -> Heterogeneous Change Detection in Remote Sensing Images. Accompanies Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
ChangeDetectionProject -> Trying out Active Learning in with deep CNNs for Change detection on remote sensing data
DSFANet -> Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
siamese-change-detection -> Targeted synthesis of multi-temporal remote sensing images for change detection using siamese neural networks
Bi-SRNet -> Pytorch codes of paper: Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
RaVAEn -> RaVAEn is a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment
SiROC -> Implementation of the paper Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images. Applied to Sentinel-2 and high-resolution Planetscope imagery on four datasets
DSMSCN -> Tensorflow implementation for Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Neural Networks
RaVAEn -> a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment. It flags changed areas to prioritise for downlink, shortening the response time
SemiCD -> Code for paper: Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images. Achieves the performance of supervised CD even with access to as little as 10% of the annotated training data
FCCDN_pytorch -> code for paper: FCCDN: Feature Constraint Network for VHR Image Change Detection. Uses the LEVIR-CD building change detection dataset
INLPG_Python -> code for paper: Structure Consistency based Graph for Unsupervised Change Detection with Homogeneous and Heterogeneous Remote Sensing Images
NSPG_Python -> code for paper: Nonlocal patch similarity based heterogeneous remote sensing change detection
LGPNet-BCD -> code for 2021 paper: Building Change Detection for VHR Remote Sensing Images via Local-Global Pyramid Network and Cross-Task Transfer Learning Strategy
DS_UNet -> code for 2021 paper: Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection using a Dual Stream U-Net, uses Onera Satellite Change Detection dataset
SiameseSSL -> code for 2022 paper: Urban change detection with a Dual-Task Siamese network and semi-supervised learning. Uses SpaceNet 7 dataset
CD-SOTA-methods -> Remote sensing change detection: State-of-the-art methods and available datasets
multimodalCD_ISPRS21 -> code for 2021 paper: Fusing Multi-modal Data for Supervised Change Detection
Unsupervised-CD-in-SITS-using-DL-and-Graphs -> code for article: Unsupervised Change Detection Analysis in Satellite Image Time Series using Deep Learning Combined with Graph-Based Approaches
LSNet -> code for 2022 paper: Extremely Light-Weight Siamese Network For Change Detection in Remote Sensing Image
Change-Detection-in-Remote-Sensing-Images -> using PCA & K-means
End-to-end-CD-for-VHR-satellite-image -> code for 2019 paper: End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++
Semantic-Change-Detection -> code for 2021 paper: SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery
ERCNN-DRS_urban_change_monitoring -> code for 2021 paper: Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data
EGRCNN -> code for 2021 paper: Edge-guided Recurrent Convolutional Neural Network for Multi-temporal Remote Sensing Image Building Change Detection
Unsupervised-Remote-Sensing-Change-Detection -> code for 2021 paper: An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning
CropLand-CD -> code for 2022 paper: A CNN-transformer Network with Multi-scale Context Aggregation for Fine-grained Cropland Change Detection
contrastive-surface-image-pretraining -> code for 2022 paper: Supervising Remote Sensing Change Detection Models with 3D Surface Semantics
dcvaVHROptical -> Deep Change Vector Analysis (DCVA) change detection. Code for 2019 paper: Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images
hyperdimensionalCD -> code for 2021 paper: Change Detection in Hyperdimensional Images Using Untrained Models
DSFANet -> code for 2018 paper: Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
FCD-GAN-pytorch -> Fully Convolutional Change Detection Framework with Generative Adversarial Network (FCD-GAN) is a framework for change detection in multi-temporal remote sensing images
DARNet-CD -> code for 2022 paper: A Densely Attentive Refinement Network for Change Detection Based on Very-High-Resolution Bitemporal Remote Sensing Images
xView2_FDNY -> Damage assessment using pre and post orthoimagery. Fifth-place model from the DIU xView2 competition
ESCNet -> code for 2021 paper: An End-to-End Superpixel-Enhanced Change Detection Network for Very-High-Resolution Remote Sensing Images
ForestCoverChange -> Detecting and Predicting Forest Cover Change in Pakistani Areas Using Remote Sensing Imagery
deforestation-detection -> code for 2020 paper: DEEP LEARNING FOR HIGH-FREQUENCY CHANGE DETECTION IN UKRAINIAN FOREST ECOSYSTEM WITH SENTINEL-2
forest_change_detection -> forest change segmentation with time-dependent models, including Siamese, UNet-LSTM, UNet-diff, UNet3D models. Code for 2021 paper: Deep Learning for Regular Change Detection in Ukrainian Forest Ecosystem With Sentinel-2
SentinelClearcutDetection -> Scripts for deforestation detection on the Sentinel-2 Level-A images
clearcut_detection -> research & web-service for clearcut detection
CDRL -> code for 2022 paper: Unsupervised Change Detection Based on Image Reconstruction Loss
ddpm-cd -> code for 2022 paper: Remote Sensing Change Detection (Segmentation) using Denoising Diffusion Probabilistic Models
Remote-sensing-time-series-change-detection -> code for 2022 paper: Graph-based block-level urban change detection using Sentinel-2 time series
austin-ml-change-detection-demo -> A change detection demo for the Austin area using a pre-trained PyTorch model scaled with Dask on Planet imagery
dfc2021-msd-baseline -> A baseline for the "Multitemporal Semantic Change Detection" track of the 2021 IEEE GRSS Data Fusion Competition
CorrFusionNet -> code for 2020 paper: Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion
ChangeDetectionPCAKmeans -> MATLAB implementation for Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering.

Time series

More general than change detection, time series observations can be used for applications including improving the accuracy of crop classification, or predicting future patterns & events. Crop yield is very typically application and has its own section below

CropDetectionDL -> using GRU-net, First place solution for Crop Detection from Satellite Imagery competition organized by CV4A workshop at ICLR 2020
LANDSAT Time Series Analysis for Multi-temporal Land Cover Classification using Random Forest
temporalCNN -> Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
pytorch-psetae -> PyTorch implementation of the model presented in Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention
satflow -> optical flow models for predicting future satellite images from current and past ones
esa-superresolution-forecasting -> Forecasting air pollution using ESA Sentinel-5p data, and an encoder-decoder convolutional LSTM neural network architecture, implemented in Pytorch
Radiant-Earth-Spot-the-Crop-Challenge -> The main objective of this challenge was to use time-series of Sentinel-2 multi-spectral data to classify crops in the Western Cape of South Africa. The challenge was to build a machine learning model to predict crop type classes for the test dataset
lightweight-temporal-attention-pytorch -> A PyTorch implementation of the Light Temporal Attention Encoder (L-TAE) for satellite image time series. classification
Crop-Classification -> crop classification using multi temporal satellite images
dtwSat -> Time-Weighted Dynamic Time Warping for satellite image time series analysis
DeepCropMapping -> A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping, uses LSTM
CropMappingInterpretation -> An interpretation pipeline towards understanding multi-temporal deep learning approaches for crop mapping
MTLCC -> code for paper: Multitemporal Land Cover Classification Network. A recurrent neural network approach to encode multi-temporal data for land cover classification
timematch -> code for 2022 paper: A method to perform unsupervised cross-region adaptation of crop classifiers trained with satellite image time series. We also introduce an open-access dataset for cross-region adaptation with SITS from four different regions in Europe
PWWB -> Code for the 2021 paper: Real-Time Spatiotemporal Air Pollution Prediction with Deep Convolutional LSTM through Satellite Image Analysis
Classification of Crop Fields through Satellite Image Time Series -> using a pytorch-psetae & Sentinel-2 data
spaceweather -> predicting geomagnetic storms from satellite measurements of the solar wind and solar corona, uses LSTMs
Forest_wildfire_spreading_convLSTM -> Modeling of the spreading of forest wildfire using a neural network with ConvLSTM cells. Prediction 3-days forward
ConvTimeLSTM -> Extension of ConvLSTM and Time-LSTM for irregularly spaced images, appropriate for Remote Sensing
dl-time-series -> Deep Learning algorithms applied to characterization of Remote Sensing time-series
tpe -> code for 2022 paper: Generalized Classification of Satellite Image Time Series With Thermal Positional Encoding
wildfire_forecasting -> code for 2021 paper: Deep Learning Methods for Daily Wildfire Danger Forecasting. Uses ConvLSTM

Crop yield

Crop yield Prediction with Deep Learning -> code for the paper Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data
Deep-Transfer-Learning-Crop-Yield-Prediction
Crop-Yield-Prediction-using-ML -> A simple Web application developed in order to provide the farmers/users an approximation on how much amount of crop yield will be produced depending upon the given input
Building a Crop Yield Prediction App in Senegal Using Satellite Imagery and Jupyter Voila
Crop Yield Prediction Using Deep Neural Networks and LSTM
Deep transfer learning techniques for crop yield prediction, published in COMPASS 2018
Understanding crop yield predictions from CNNs
Advanced Deep Learning Techniques for Predicting Maize Crop Yield using Sentinel-2 Satellite Imagery
pycrop-yield-prediction -> A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction
PredictYield -> using data scraped from Google Earth Engine, this predicts the yield of Corn, Soybean, and Wheat in the USA with Keras
Crop-Yield-Prediction-and-Estimation-using-Time-series-remote-sensing-data -> student research
Yield-Prediction-Using-Sentinel-Data

Wealth and economic activity

The goal is to predict economic activity from satellite imagery rather than conducting labour intensive ground surveys

Using publicly available satellite imagery and deep learning to understand economic well-being in Africa, Nature Comms 22 May 2020 -> Used CNN on Ladsat imagery (night & day) to predict asset wealth of African villages
Combining Satellite Imagery and machine learning to predict poverty -> review article
Measuring Human and Economic Activity from Satellite Imagery to Support City-Scale Decision-Making during COVID-19 Pandemic -> arxiv article
Predicting Food Security Outcomes Using CNNs for Satellite Tasking -> arxiv article
Measuring the Impacts of Poverty Alleviation Programs with Satellite Imagery and Deep Learning -> code and paper
Building a Spatial Model to Classify Global Urbanity Levels -> estimage global urbanity levels from population data, nightime lights and road networks
deeppop -> Deep Learning Approach for Population Estimation from Satellite Imagery, also on Github
Estimating telecoms demand in areas of poor data availability -> with papers on arxiv and Science Direct
satimage -> Code and models for the manuscript "Predicting Poverty and Developmental Statistics from Satellite Images using Multi-task Deep Learning". Predict the main material of a roof, source of lighting and source of drinking water for properties, from satellite imagery
africa_poverty -> Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
Predicting-Poverty -> Combining satellite imagery and machine learning to predict poverty, in PyTorch
income-prediction -> Predicting average yearly income based on satellite imagery using CNNs, uses pytorch
urban_score -> Pytorch Implementation of paper: Learning to score economic development from satellite imagery
READ -> Pytorch Implementation of paper: Lightweight and robust representation of economic scales from satellite imagery
Slum-classification -> Binary classification on a very high-resolution satellite image in case of mapping informal settlements using unet
Predicting_Poverty -> uses daytime & luminosity of nighttime satellite images
Cancer-Prevalence-Satellite-Images -> Predict Health Outcomes from Features of Satellite Images
Mapping Poverty in Bangladesh with Satellite Images and Deep Learning -> combines health data with OpenStreetMaps Data & night and daytime satellite imagery
Population Estimation from Satellite Imagery
Deep_Learning_Satellite_Imd -> code for "Project Bhoomi" - Using Deep Learning on Satellite Imagery to predict population and economic indicators
satellite_led_liverpool -> code for 2017 paper: Remote Sensing-Based Measurement of Living Environment Deprivation - Improving Classical Approaches with Machine Learning
uganda-poverty-project -> use through Object Detection on high-resolution satellite imagery to identify indicators of poverty and economic inequality within Uganda
Predicting_Energy_Consumption_With_Convolutional_Neural_Networks

Disaster response

Also checkout the sections on change detection and water/fire/building segmentation

DisaVu -> combines building & damage detection and provides an app for viewing predictions
Soteria -> uses machine learning with satellite imagery to map natural disaster impacts for faster emergency response
DisasterHack -> Wildfire Mitigation: Computer Vision Identification of Hazard Fuels Using Landsat
forestcasting -> Forest fire prediction powered by analytics
Machine Learning-based Damage Assessment for Disaster Relief on Google AI blog -> uses object detection to locate buildings, then a classifier to determine if a building is damaged. Challenge of generalising due to small dataset
RaVAEn -> RaVAEn is a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment
hurricane_damage -> Post-hurricane structure damage assessment based on aerial imagery with CNN
rescue -> code of the paper: Attention to fires: multi-channel deep-learning models forwildfire severity prediction
Disaster-Classification -> A disaster classification model to predict the type of disaster given an input image, trained on this dataset
Coarse-to-fine weakly supervised learning method for green plastic cover segmentation -> with paper
Detection of destruction in satellite imagery
BDD-Net -> code for 2020 paper: A General Protocol for Mapping Buildings Damaged by a Wide Range of Disasters Based on Satellite Imagery
Automatic_Disaster_Detection -> detect the affected area by natural disasters by using the way of semantic segmentation and change detection method
Flooding Damage Detection from Post-Hurricane Satellite Imagery Based on Convolutional Neural Networks
IBM-Disaster-Response-Hack -> identifying optimal terrestrial routes through calamity-stricken areas. Satellite image data informs road condition assessment and obstruction detection
Automatic Damage Annotation on Post-Hurricane Satellite Imagery -> detect damaged buildings using tensorflow object detection API. With repos here and here
Hurricane-Damage-Detection -> Waterloo's Hack the North 2020++ submission. A convolutional neural network model used to detect hurricane damage in RGB satellite images
wildfire_forecasting -> code for 2021 paper: Deep Learning Methods for Daily Wildfire Danger Forecasting. Uses ConvLSTM

Weather phenomena

EddyData -> code for paper: A Deep Framework for Eddy Detection and Tracking from Satellite Sea Surface Height Data
python-windspeed -> Predicting windspeed of hurricanes from satellite images, uses CNN regression in keras
hurricane-wind-speed-cnn -> Predicting windspeed of hurricanes from satellite images, uses CNN regression in keras

Super-resolution

Super-resolution attempts to enhance the resolution of an imaging system, and can be applied as a pre-processing step to improve the detection of small objects or boundaries. Its use is controversial since it can introduce artefacts at the same rate as real features. These techniques are generally grouped into single image super resolution (SISR) or a multi image super resolution (MISR)

The value of super resolution — real world use case -> Medium article on parcel boundary detection with super-resolved satellite imagery
Super-Resolution on Satellite Imagery using Deep Learning -> Nov 2016 blog post by CosmiQ Works with a nice introduction to the topic. Proposes and demonstrates a new architecture with perturbation layers with practical guidance on the methodology and code. Three part series
Introduction to spatial resolution
Awesome-Super-Resolution -> another 'awesome' repo, getting a little out of date now
Super-Resolution (python) Utilities for managing large satellite images
pytorch-enhance -> Library of Image Super-Resolution Models, Datasets, and Metrics for Benchmarking or Pretrained Use. Also checkout this implementation in Jax
Super Resolution in OpenCV
AI-based Super resolution and change detection to enforce Sentinel-2 systematic usage -> Worldview-2 images (2m) were used to create a reference dataset and increase the spatial resolution of the Copernicus sensor from 10m to 5m
SRCDNet -> The pytorch implementation for "Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions ". SRCDNet is designed to learn and predict change maps from bi-temporal images with different resolutions
Model-Guided Deep Hyperspectral Image Super-resolution -> code accompanying the paper Model-Guided Deep Hyperspectral Image Super-Resolution
Super-resolving beyond satellite hardware -> paper assessing SR performance in reconstructing realistically degraded satellite images
satellite-pixel-synthesis-pytorch -> PyTorch implementation of NeurIPS 2021 paper: Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
SRE-HAN -> Squeeze-and-Residual-Excitation Holistic Attention Network improves super-resolution (SR) on remote-sensing imagery compared to other state-of-the-art attention-based SR models
satsr -> A project to perform super-resolution on multispectral images from any satellite, including Sentinel 2, Landsat 8, VIIRS &MODIS
OLI2MSI -> dataset for remote sensing imagery super-resolution composed of Landsat8-OLI and Sentinel2-MSI images
MMSR -> Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution
HSRnet -> code for the 2021 paper: Hyperspectral Image Super-resolution via Deep Spatio-spectral Attention Convolutional Neural Networks

Single image super-resolution (SISR)

Super Resolution for Satellite Imagery - srcnn repo
TensorFlow implementation of "Accurate Image Super-Resolution Using Very Deep Convolutional Networks" adapted for working with geospatial data
Random Forest Super-Resolution (RFSR repo) including sample data
Enhancing Sentinel 2 images by combining Deep Image Prior and Decrappify. Repo for deep-image-prior and article on decrappify
Image Super-Resolution using an Efficient Sub-Pixel CNN -> the keras docs have a great tutorial on this light weight but well performing model
super-resolution-using-gan -> Super-Resolution of Sentinel-2 Using Generative Adversarial Networks
Super-resolution of Multispectral Satellite Images Using Convolutional Neural Networks with paper
Multi-temporal Super-Resolution on Sentinel-2 Imagery using HighRes-Net, repo
SSPSR-Pytorch -> A spatial-spectral prior deep network for single hyperspectral image super-resolution
Sentinel-2 Super-Resolution: High Resolution For All (Bands)
CinCGAN -> Unofficial Implementation of Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
Satellite-image-SRGAN using PyTorch
EEGAN -> Edge Enhanced GAN For Remote Sensing Image Super-Resolution, TensorFlow 1.1
PECNN -> A Progressively Enhanced Network for Video Satellite Imagery Super-Resolution, minimal documentation
hs-sr-tvtv -> Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization
sr4rs -> Super resolution for remote sensing, with pre-trained model for Sentinel-2, SRGAN-inspired
Restoring old aerial images with Deep Learning -> Medium article on Super Resolution with Perceptual Loss function and real images as input
RFSR_TGRS -> code for the paper Hyperspectral Image Super-Resolution via Recurrent Feedback Embedding and Spatial-Spectral Consistency Regularization
SEN2VENµS -> a dataset for the training of Sentinel-2 super-resolution algorithms
TransENet -> code for 2021 paper: Transformer-based Multi-Stage Enhancement for Remote Sensing Image Super-Resolution
SG-FBGAN -> code for 2020 paper: Remote Sensing Image Super-Resolution via Saliency-Guided Feedback GANs
finetune_ESRGAN -> finetune the ESRGAN super resolution generator for remote sensing images and video
MIP -> code for 2021 paper: Unsupervised Remote Sensing Super-Resolution via Migration Image Prior
Optical-RemoteSensing-Image-Resolution -> code for 2018 paper: Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration. Two applications: Gaussian image denoising and single image super-resolution
HSENet -> code for 2021 paper: Hybrid-Scale Self-Similarity Exploitation for Remote Sensing Image Super-Resolution
SR_RemoteSensing -> Super-Resolution deep learning models for remote sensing data based on BasicSR
RSI-Net -> code for 2022 paper: A Deep Multi-task Convolutional Neural Network for Remote Sensing Image Super-resolution and Colorization
EDSR-Super-Resolution -> EDSR model using PyTorch applied to satellite imagery
CycleCNN -> code for 2021 paper: Nonpairwise-Trained Cycle Convolutional Neural Network for Single Remote Sensing Image Super-Resolution
SISR with with Real-World Degradation Modeling -> code for 2022 paper: Single-Image Super Resolution of Remote Sensing Images with Real-World Degradation Modeling

Multi image super-resolution (MISR)

Note that nearly all the MISR publications resulted from the PROBA-V Super Resolution competition

deepsum -> Deep neural network for Super-resolution of Unregistered Multitemporal images (ESA PROBA-V challenge)
3DWDSRNet -> code to reproduce Satellite Image Multi-Frame Super Resolution (MISR) Using 3D Wide-Activation Neural Networks
RAMS -> Official TensorFlow code for paper Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks
TR-MISR -> Transformer-based MISR framework for the the PROBA-V super-resolution challenge
HighRes-net -> Pytorch implementation of HighRes-net, a neural network for multi-frame super-resolution, trained and tested on the European Space Agency’s Kelvin competition
ProbaVref -> Repurposing the Proba-V challenge for reference-aware super resolution
The missing ingredient in deep multi-temporal satellite image super-resolution -> Permutation invariance harnesses the power of ensembles in a single model, with repo piunet
MSTT-STVSR -> Space-time Super-resolution for Satellite Video: A Joint Framework Based on Multi-Scale Spatial-Temporal Transformer, JAG, 2022
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites
DDRN -> Deep Distillation Recursive Network for Video Satellite Imagery Super-Resolution

Pansharpening

Image fusion of low res multispectral with high res pan band.

Several algorithms described in the ArcGIS docs, with the simplest being taking the mean of the pan and RGB pixel value.
For into to classical methods see this notebook and this kaggle kernel
rio-pansharpen -> pansharpening Landsat scenes
Simple-Pansharpening-Algorithms
Working-For-Pansharpening -> long list of pansharpening methods and update of Awesome-Pansharpening
PSGAN -> A Generative Adversarial Network for Remote Sensing Image Pan-sharpening, arxiv paper
Pansharpening-by-Convolutional-Neural-Network
PBR_filter -> {P}ansharpening by {B}ackground {R}emoval algorithm for sharpening RGB images
py_pansharpening -> multiple algorithms implemented in python
Deep-Learning-PanSharpening -> deep-learning based pan-sharpening code package, we reimplemented include PNN, MSDCNN, PanNet, TFNet, SRPPNN, and our purposed network DIPNet
HyperTransformer -> A Textural and Spectral Feature Fusion Transformer for Pansharpening
DIP-HyperKite -> Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction
D2TNet -> code for 2022 paper: A ConvLSTM Network with Dual-direction Transfer for Pan-sharpening
PanColorGAN-VHR-Satellite-Images -> code for 2020 paper: Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs
MTL_PAN_SEG -> code for 2019 paper: Multi-task deep learning for satellite image pansharpening and segmentation
Z-PNN -> code for 2022 paper: Pansharpening by convolutional neural networks in the full resolution framework
GTP-PNet -> code for 2021 paper: GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening
UDL -> code for 2021 paper: Dynamic Cross Feature Fusion for Remote Sensing Pansharpening
PSData -> A Large-Scale General Pan-sharpening DataSet, which contains PSData3 (QB, GF-2, WV-3) and PSData4 (QB, GF-1, GF-2, WV-2).
AFPN -> Adaptive Detail Injection-Based Feature Pyramid Network For Pan-sharpening
pan-sharpening -> multiple methods demonstrated for multispectral and panchromatic images
PSGan-Family -> code for 2020 paper: PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening
PanNet-Landsat -> code for 2017 paper: A Deep Network Architecture for Pan-Sharpening
DLPan-Toolbox -> code for 2022 paper: Machine Learning in Pansharpening: A Benchmark, from Shallow to Deep Networks
LPPN -> code for 2021 paper: Laplacian pyramid networks: A new approach for multispectral pansharpening

Image-to-image translation

Translate images e.g. from SAR to RGB.

How to Develop a Pix2Pix GAN for Image-to-Image Translation -> how to develop a Pix2Pix model for translating satellite photographs to Google map images. A good intro to GANS
A growing problem of ‘deepfake geography’: How AI falsifies satellite images
Kaggle Pix2Pix Maps -> dataset for pix2pix to take a google map satellite photo and build a street map
guided-deep-decoder -> With guided deep decoder, you can solve different image pair fusion problems, allowing super-resolution, pansharpening or denoising
hackathon-ci-2020 -> generate nighttime imagery from infrared observations
satellite-to-satellite-translation -> VAE-GAN architecture for unsupervised image-to-image translation with shared spectral reconstruction loss. Model is trained on GOES-16/17 and Himawari-8 L1B data
Pytorch implementation of UNet for converting aerial satellite images into google maps kinda images
Seamless-Satellite-image-Synthesis -> generate abitrarily large RGB images from a map
How to Develop a Pix2Pix GAN for Image-to-Image Translation -> article on machinelearningmastery.com
Satellite-Imagery-to-Map-Translation-using-Pix2Pix-GAN-framework
SAR to RGB Translation using CycleGAN -> uses a CycleGAN model in the ArcGIS API for Python
RSIT_SRM_ISD -> PyTorch implementation of Remote sensing image translation via style-based recalibration module and improved style discriminator
pix2pix_google_maps -> Converts satellite images to map images using pix2pix models
sar2color-igarss2018-chainer -> code for 2018 paper: Image Translation Between Sar and Optical Imagery with Generative Adversarial Nets
HSI2RGB -> Create realistic looking RGB images using remote sensing hyperspectral images
sat_to_map -> Learning mappings to generate city maps images from corresponding satellite images
pix2pix-GANs -> Generate Map using Satellite Image & PyTorch

GANS

GANS are famously used for generating synthetic data, see the section Synthetic data

Anomaly Detection on Mars using a GAN
Using Generative Adversarial Networks to Address Scarcity of Geospatial Training Data -> GAN perform better than CNN in segmenting land cover classes outside of the training dataset (article, no code)
Building-A-Nets -> robust building extraction from high-resolution remote sensing images with adversarial networks
GANmapper -> a building footprint generator using Generative Adversarial Networks
CSA-CDGAN -> Channel Self-Attention Based Generative Adversarial Network for Change Detection of Remote Sensing Images
DSGAN -> a conditinal GAN for dynamic precipitation downscaling
MarsGAN -> GAN trained on satellite photos of Mars
HC_ADGAN -> codes for the paper Adaptive Dropblock Enhanced GenerativeAdversarial Networks for Hyperspectral Image Classification
SCALAE -> code for our paper Formatting the Landscape: Spatial conditional GAN for varying population in satellite imagery. Method to generate satellite imagery from custom 2D population maps
Satellite-Image-Forgery-Detection-and-Localization
STGAN -> PyTorch Implementation of STGAN for Cloud Removal in Satellite Images, with paper
ds-gan-spatiotemporal-evaluation -> evaluating use of deep generative models in remote sensing applications
pub-ffi-gan -> code for 2018 paper: Applying generative adversarial networks for anomaly detection in hyperspectral remote sensing imagery
GAN-based method to generate high-resolution remote sensing for data augmentation and image classification
Remote-Sensing-Image-Generation -> Generate RS Images using Generative Adversarial Networks (GAN)
RoadDA -> code for 2021 paper: Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images
PSGan-Family -> code for 2020 paper: PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening

Adversarial ML

Efforts to detect falsified images & deepfakes. Also checkout Synthetic data

UAE-RS -> dataset that provides black-box adversarial samples in the remote sensing field
PSGAN -> code for paper: Perturbation Seeking Generative Adversarial Networks: A Defense Framework for Remote Sensing Image Scene Classification

Autoencoders, dimensionality reduction, image embeddings & similarity search

Autoencoders & their Application in Remote Sensing -> intro article and example use case applied to SAR data for land classification
LEt-SNE -> Dimensionality Reduction and visualization technique that compensates for the curse of dimensionality
AutoEncoders for Land Cover Classification of Hyperspectral Images -> An autoencoder nerual net is used to reduce 103 band data to 60 features (dimensionality reduction), keras. Also read part 2 which implements K-NNC, SVM and Gradient Boosting
Image-Similarity-Search -> an app that helps perform super fast image retrieval on PyTorch models for better embedding space interpretability
Interactive-TSNE -> a tool that provides a way to visually view a PyTorch model's feature representation for better embedding space interpretability
How Airbus Detects Anomalies in ISS Telemetry Data Using TFX -> uses an autoencoder
RoofNet -> identify roof age using historical satellite images to lower the customer acquisition cost for new solar installations. Uses a VAE: Variational Autoencoder
Visual search over billions of aerial and satellite images -> implemented at Descartes labs
parallax -> Tool for interactive embeddings visualization
Deep-Gapfill -> Official implementation of Optical image gap filling using deep convolutional autoencoder from optical and radar images
Mxnet repository for generating embeddings on satellite images -> Includes sampling of images, mining algorithms, different architectures, error functions, measures for evaluation.
Fine tuning CLIP with Remote Sensing (Satellite) images and captions -> fine tuning CLIP on the RSICD image captioning dataset, to enable querying large catalogues in natural language. With repo, uses 🤗
Image search with 🤗 datasets -> tutorial on fine tuning an image search model
SynImageAnalysis -> comparing syn and real sattlelite images in the latent feature space (embeddings)
GRN-SNDL -> model the relations between samples (or scenes) by making use of a graph structure which is fed into network learning
SauMoCo -> codes for TGRS paper: Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast
TGRS_RiDe -> Rotation Invariant Deep Embedding for RemoteSensing Images
RaVAEn -> RaVAEn is a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment
Reverse image search using deep discrete feature extraction and locality-sensitive hashing
SNCA_CE -> code for the paper Deep Metric Learning based on Scalable Neighborhood Components for Remote Sensing Scene Characterization
LandslideDetection-from-satellite-imagery -> Using Attention and Autoencoder boosted CNN
split-brain-remote-sensing -> code for 2018 paper: Analysis of Color Space Quantization in Split-Brain Autoencoder for Remote Sensing Image Classification
image-similarity-measures -> Implementation of eight evaluation metrics to access the similarity between two images. Blog post here
Large_Scale_GeoVisual_Search -> ResNet architecture on UC Merced Land Use Dataset with hamming distance for similarity based search
geobacter -> Generates useful feature embeddings for geospatial locations
Satellite-Image-Segmentation -> the KV-Net model uses this feature of autoencoders to reconnect the disconnected roads
Satellite-Image-Enhancement -> Image enhancement using GAN's and autoencoders
Variational-Autoencoder-For-Satellite-Imagery -> a special VAE to squeeze N images into one single representation with colors segmentating the different objects
DINCAE -> Data-Interpolating Convolutional Auto-Encoder is a neural network to reconstruct missing data in satellite observations
3D_SITS_Clustering -> code for 2020 paper: Unsupervised Satellite Image Time Series Clustering Using Object-Based Approaches and 3D Convolutional Autoencoder
sat_cnn -> code for 2022 paper: Estimating Generalized Measures of Local Neighbourhood Context from Multispectral Satellite Images Using a Convolutional Neural Network. Uses a convolutional autoencoder (CAE)
you-are-here -> Matlab code for 2020 paper: You Are Here: Geolocation by Embedding Maps and Images
Tensorflow similarity -> offers state-of-the-art algorithms for metric learning and all the necessary components to research, train, evaluate, and serve similarity-based models
Train SimSiam on Satellite Images using Lightly to generate embeddings that can be used for data exploration and understanding
Airbus_SDC_dup -> Project focused on detecting duplicate regions of overlapping satellite imagery. Applied to Airbus ship detection dataset

Image retreival

Demo_AHCL_for_TGRS2022 -> code for 2022 paper: Asymmetric Hash Code Learning (AHCL) for remote sensing image retreival
GaLR -> code for 2022 paper: Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information
retrievalSystem -> cross-modal image retrieval system
AMFMN -> code for the 2021 paper: Exploring a Fine-grained Multiscale Method for Cross-modal Remote Sensing Image Retrieval
Active-Learning-for-Remote-Sensing-Image-Retrieval -> unofficial implementation of paper: A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval
CMIR-NET -> code for 2020 paper: A deep learning based model for cross-modal retrieval in remote sensing
Deep-Hash-learning-for-Remote-Sensing-Image-Retrieval -> code for 2020 paper: Deep Hash Learning for Remote Sensing Image Retrieval
MHCLN -> code for 2018 paper: Deep Metric and Hash-Code Learning for Content-Based Retrieval of Remote Sensing Images
HydroViet_VOR -> Object Retrieval in satellite images with Triplet Network
AMFMN -> code for 2021 paper: Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval

Image Captioning & Visual Question Answering

See the section Image captioning datasets
remote-sensing-image-caption -> image classification and image caption by PyTorch
Fine tuning CLIP with Remote Sensing (Satellite) images and captions -> fine tuning CLIP on the RSICD image captioning dataset, to enable querying large catalogues in natural language. With repo, uses 🤗
VQA-easy2hard -> code for 2022 paper: From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data
CapFormer -> Pure transformer for remote sensing image caption
remote_sensing_image_captioning -> code for 2019 paper: Region Driven Remote Sensing Image Captioning
Remote Sensing Image Captioning with Transformer and Multilabel Classification
Siamese-spatial-Graph-Convolution-Network -> code for 2019 paper: Siamese graph convolutional network for content based remote sensing image retrieval
MLAT -> code for 2022 paper: Remote-Sensing Image Captioning Based on Multilayer Aggregated Transformer
WordSent -> code for 2020 paper: Word–Sentence Framework for Remote Sensing Image Captioning
a-mask-guided-transformer-with-topic-token -> code for 2022 paper: A Mask-Guided Transformer Network with Topic Token for Remote Sensing Image Captioning

Mixed data learning

These techniques combine multiple data types, e.g. imagery and text data.

Predicting the locations of traffic accidents with satellite imagery and convolutional neural networks -> Combining satellite imagery and structured data to predict the location of traffic accidents with a neural network of neural networks, with repo
Multi-Input Deep Neural Networks with PyTorch-Lightning - Combine Image and Tabular Data -> excellent intro article using pytorch, not actually applied to satellite data but to real estate data, with repo
Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps -> fusion based architectures and coarse-to-fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks, arxiv paper
Inferring High-Resolution Traffic Accident Risk Maps Based on Satellite Imagery and GPS Trajectories -> input satellite imagery, GPS trajectories, road maps and the history of accidents to generate high-resolution (5 meters) accident risk maps
Composing Decision Forest and Neural Network models tensorflow documentation
pyimagesearch article on mixed-data
pytorch-widedeep -> A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
accidentRiskMap -> Inferring high-resolution traffic accident risk maps based on satellite imagery and GPS trajectories

Few-shot learning

This is a class of techniques which attempt to make predictions for classes with few, one or even zero examples provided during training. In zero shot learning (ZSL) the model is assisted by the provision of auxiliary information which typically consists of descriptions/semantic attributes/word embeddings for both the seen and unseen classes at train time (ref). These approaches are particularly relevant to remote sensing, where there may be many examples of common classes, but few or even zero examples for other classes of interest.

Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks
FSODM -> Official Code for paper "Few-shot Object Detection on Remote Sensing Images" on arxiv
Few-Shot Classification of Aerial Scene Images via Meta-Learning -> 2020 publication, a classification model that can quickly adapt to unseen categories using only a few labeled samples
Papers about Few-shot Learning / Meta-Learning on Remote Sensing
SPNet -> code for 2021 paper: Siamese-Prototype Network for Few-Shot Remote Sensing Image Scene Classification
MDL4OW -> code for 2020 paper: Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning
P-CNN -> code for 2021 paper: Prototype-CNN for Few-Shot Object Detection in Remote Sensing Images
CIR-FSD-2022 -> code for 2022 paper: Context Information Refinement for Few-Shot Object Detection in Remote Sensing Images
IEEE_TNNLS_Gia-CFSL -> code for 2022 paper: Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification

Self-supervised, unsupervised & contrastive learning

These techniques use unlabelled datasets. Yann LeCun has described self/unsupervised learning as the 'base of the cake': If we think of our brain as a cake, then the cake base is unsupervised learning. The machine predicts any part of its input for any observed part, all without the use of labelled data. Supervised learning forms the icing on the cake, and reinforcement learning is the cherry on top.

Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data -> Seasonal Contrast (SeCo) is an effective pipeline to leverage unlabeled data for in-domain pre-training of remote sensing representations. Models trained with SeCo achieve better performance than their ImageNet pre-trained counterparts and state-of-the-art self-supervised learning methods on multiple downstream tasks. paper and repo
Unsupervised Learning for Land Cover Classification in Satellite Imagery
Tile2Vec: Unsupervised representation learning for spatially distributed data
Contrastive Sensor Fusion -> Code implementing Contrastive Sensor Fusion, an approach for unsupervised learning of multi-sensor representations targeted at remote sensing imagery
hyperspectral-autoencoders -> Tools for training and using unsupervised autoencoders and supervised deep learning classifiers for hyperspectral data, built on tensorflow. Autoencoders are unsupervised neural networks that are useful for a range of applications such as unsupervised feature learning and dimensionality reduction.
Sentinel-2 image clustering in python
MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification and code
A generalizable and accessible approach to machine learning with global satellite imagery nature publication -> MOSAIKS is designed to solve an unlimited number of tasks at planet-scale quickly using feature vectors, with repo. Also see mosaiks-api
contrastive-satellite -> Using contrastive learning to create embeddings from optical EuroSAT Satellite-2 imagery
Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding -> arxiv paper and code
Self-Supervised-Learner by spaceml-org -> train a classifier with fewer labeled examples needed using self-supervised learning, example applied to UC Merced land use dataset
deepsentinel -> a sentinel-1 and -2 self-supervised sensor fusion model for general purpose semantic embedding
contrastive_SSL_ship_detection -> Contrastive self supervised learning for ship detection in Sentinel 2 images
geography-aware-ssl -> uses spatially aligned images over time to construct temporal positive pairs in contrastive learning and geo-location to design pre-text tasks
CNN-Supervised Classification -> Python code for self-supervised classification of remotely sensed imagery - part of the Deep Riverscapes project
clustimage -> a python package for unsupervised clustering of images
LandSurfaceClustering -> Land surface classification using remote sensing data with unsupervised machine learning (k-means)
K-Means Clustering for Surface Segmentation of Satellite Images
Sentinel-2 satellite imagery for crop classification using unsupervised clustering -> label groups of pixels based on temporal trends of their NDVI values
TheColorOutOfSpace -> Pytorch code for the paper "The color out of space: learning self-supervised representations for Earth Observation imagery" using the BigEarthNet dataset
Semantic segmentation of SAR images using a self supervised technique
STEGO -> Unsupervised Semantic Segmentation by Distilling Feature Correspondences, with paper
Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels
SoundingEarth -> Self-supervised Audiovisual Representation Learning for Remote Sensing Data, uses the SoundingEarth Dataset
singleSceneSemSegTgrs2022 -> code for 2022 paper: Unsupervised Single-Scene Semantic Segmentation for Earth Observation
SSLRemoteSensing -> code for 2021 paper: Semantic Segmentation of Remote Sensing Images With Self-Supervised Multitask Representation Learning
CBT code for 2022 paper: Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation
Unsupervised Satellite Image Classification based on Partial Adversarial Domain Adaptation -> Code for course project
selfsup_openrep -> self-supervised learning method framework for domain representation
T2FTS -> code for 2022 paper: Teaching Teachers First and Then Student: Hierarchical Distillation to Improve Long-Tailed Object Recognition in Aerial Images
SSLTransformerRS -> code for 2022 paper: Self-supervised Vision Transformers for Land-cover Segmentation and Classification
DINO-MM -> code for 2022 paper: Self-supervised Vision Transformers for Joint SAR-optical Representation Learning
SSL4EO-S12 -> a large-scale dataset for self-supervised learning in Earth observation
SSL4EO-Review -> code for 2022 paper: Self-supervised Learning in Remote Sensing: A Review
transfer_learning_cspt -> code for 2022 paper: Consecutive Pretraining: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain
OTL -> code for 2022 paper: Clustering-Based Representation Learning through Output Translation and Its Application to Remote-Sensing Images
Push-and-Pull-Network -> code for 2022 paper: Contrastive Learning for Fine-grained Ship Classification in Remote Sensing Images

Weakly & semi-supervised learning

These techniques use a partially annotated dataset

MARE -> self-supervised Multi-Attention REsu-net for semantic segmentation in remote sensing
SSGF-for-HRRS-scene-classification -> code for 2018 paper: A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification
SFGAN -> code for 2018 paper: Semantic-Fusion Gans for Semi-Supervised Satellite Image Classification
SSDAN -> code for 2021 paper: Multi-Source Semi-Supervised Domain Adaptation Network for Remote Sensing Scene Classification
HR-S2DML -> code for 2020 paper: High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery
Semantic Segmentation of Satellite Images Using Point Supervision
fcd -> code for 2021 paper: Fixed-Point GAN for Cloud Detection. A weakly-supervised approach, training with only image-level labels
weak-segmentation -> Weakly supervised semantic segmentation for aerial images in pytorch
TNNLS_2022_X-GPN -> Code for paper: Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification
weakly_supervised -> code for the paper Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery. Demonstrates that segmentation can be performed using small datasets comprised of pixel or image labels
wan -> Weakly-Supervised Domain Adaptation for Built-up Region Segmentation in Aerial and Satellite Imagery, with arxiv paper
sourcerer -> A Bayesian-inspired deep learning method for semi-supervised domain adaptation designed for land cover mapping from satellite image time series (SITS). Paper
MSMatch -> Semi-Supervised Multispectral Scene Classification with Few Labels. Includes code to work with both the RGB and the multispectral (MS) versions of EuroSAT dataset and the UC Merced Land Use (UCM) dataset. Paper
Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning with arxiv paper
Semi-supervised learning in satellite image classification -> experimenting with MixMatch and the EuroSAT data set
ScRoadExtractor -> code for 2020 paper: Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images

Active learning

Supervised deep learning techniques typically require a huge number of annotated/labelled examples to provide a training dataset. However labelling at scale take significant time, expertise and resources. Active learning techniques aim to reduce the total amount of annotation that needs to be performed by selecting the most useful images to label from a large pool of unlabelled images, thus reducing the time to generate useful training datasets. These processes may be referred to as Human-in-the-Loop Machine Learning

Active learning for object detection in high-resolution satellite images -> arxiv paper
AIDE V2 - Tools for detecting wildlife in aerial images using active learning
AstronomicAL -> An interactive dashboard for visualisation, integration and classification of data using Active Learning
Read about active learning on the lightly platform and in label-studio
Active-Labeler by spaceml-org -> a CLI Tool that facilitates labeling datasets with just a SINGLE line of code
Labelling platform for Mapping Africa active learning project
ChangeDetectionProject -> Trying out Active Learning in with deep CNNs for Change detection on remote sensing data
ALS4GAN -> Active Learning for Improved Semi Supervised Semantic Segmentation in Satellite Images, with paper
Active-Learning-for-Remote-Sensing-Image-Retrieval -> unofficial implementation of paper: A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval

Federated learning

Federated learning is a process for training models in a distributed fashion without sharing of data

Federated-Learning-for-Remote-Sensing -> implementation of three Federated Learning models

Image registration

Image registration is the process of registering one or more images onto another (typically well georeferenced) image. Traditionally this is performed manually by identifying control points (tie-points) in the images, for example using QGIS. This section lists approaches which mostly aim to automate this manual process. There is some overlap with the data fusion section but the distinction I make is that image registration is performed as a prerequisite to downstream processes which will use the registered data as an input.

Wikipedia article on registration -> register for change detection or image stitching
Phase correlation is used to estimate the XY translation between two images with sub-pixel accuracy. Can be used for accurate registration of low resolution imagery onto high resolution imagery, or to register a sub-image on a full image -> Unlike many spatial-domain algorithms, the phase correlation method is resilient to noise, occlusions, and other defects. With additional pre-processing image rotation and scale changes can also be calculated.
ImageRegistration -> Interview assignment for multimodal image registration using SIFT
imreg_dft -> Image registration using discrete Fourier transform. Given two images it can calculate the difference between scale, rotation and position of imaged features. Used by the up42 co-registration service
arosics -> Perform automatic subpixel co-registration of two satellite image datasets using phase-correlation, XY translations only.
SubpixelAlignment -> Implementation of tiff image alignment through phase correlation for pixel- and subpixel-bias
cnn-registration -> A image registration method using convolutional neural network features written in Python2, Tensorflow 1.5
Detecting Ground Control Points via Convolutional Neural Network for Stereo Matching -> code?
Image Registration: From SIFT to Deep Learning -> background reading on has the state of the art has evolved from OpenCV to Neural Networks
ImageCoregistration -> Image registration with openCV using sift and RANSAC
mapalignment -> Aligning and Updating Cadaster Maps with Remote Sensing Images
CVPR21-Deep-Lucas-Kanade-Homography -> deep learning pipeline to accurately align challenging multimodality images. The method is based on traditional Lucas-Kanade algorithm with feature maps extracted by deep neural networks.
eolearn implements phase correlation, feature matching and ECC
RStoolbox supports Image to Image Co-Registration based on Mutual Information
Reprojecting the Perseverance landing footage onto satellite imagery
Kornia provides image registration by gradient decent
LoFTR -> Detector-Free Local Feature Matching with Transformers. Good performance matching satellite image pairs, tryout the web demo on your data
image-to-db-registration -> This remote module implements an algorithm for automated vector Database registration onto an Image. Implemented in the orfeo-toolbox
MS_HLMO_registration -> Multi-scale Histogram of Local Main Orientation for Remote Sensing Image Registration, with paper
cnn-matching -> code and datadset for paper: Deep learning algorithm for feature matching of cross modality remote sensing images
Imatch-P -> A demo using SuperGlue and SuperPoint to do the image matching task based PaddlePaddle
NBR-Net -> A Non-rigid Bi-directional Registration Network for Multi-temporal Remote Sensing Images
MU-Net -> code for paper: A Multi-Scale Framework with Unsupervised Learning for Remote Sensing Image Registration. Also checkout this implementation
unsupervisedDeepHomographyRAL2018 -> Unsupervised Deep Homography applied to aerial data
registration_cnn_ntg -> code for paper: A Multispectral Image Registration Method Based on Unsupervised Learning
remote-sensing-images-registration-dataset -> at 0.23m, 3.75m & 30m resolution
semantic-template-matching -> code for 2021 paper: A deep learning semantic template matching framework for remote sensing image registration
GMN-Generative-Matching-Network -> code for 2018 paper: Deep Generative Matching Network for Optical and SAR Image Registration
SOMatch -> code for 2020 paper: A deep learning framework for matching of SAR and optical imagery
Interspectral image registration dataset -> including satellite and drone imagery
RISG-image-matching -> A rotation invariant SuperGlue image matching algorithm
DeepAerialMatching_pytorch -> code for 2020 paper: A Two-Stream Symmetric Network with Bidirectional Ensemble for Aerial Image Matching
DPCN -> code for 2020 paper: Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching
FSRA -> code for 2022 paper: A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization
IHN -> code for 2022 paper: Iterative Deep Homography Estimation
OSMNet -> code for 2021 paper: Explore Better Network Framework for High-Resolution Optical and SAR Image Matching

Data fusion

Data fusion covers techniques which integrate multiple datasources, for example fusing SAR & optical to make predictions about crop type. It can also cover fusion with non imagery data such as IOT sensor data

Awesome-Data-Fusion-for-Remote-Sensing
UDALN_GRSL -> Deep Unsupervised Blind Hyperspectral and Multispectral Data Fusion
CropTypeMapping -> Crop type mapping from optical and radar (Sentinel-1&2) time series using attention-based deep learning
Multimodal-Remote-Sensing-Toolkit -> uses Hyperspectral and LiDAR Data
Aerial-Template-Matching -> development of an algorithm for template Matching on aerial imagery applied to UAV dataset
DS_UNet -> code for 2021 paper: Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection using a Dual Stream U-Net, uses Onera Satellite Change Detection dataset
DDA_UrbanExtraction -> Unsupervised Domain Adaptation for Global Urban Extraction using Sentinel-1 and Sentinel-2 Data
swinstfm -> code for paper: Remote Sensing Spatiotemporal Fusion using Swin Transformer
LoveCS -> code for 2022 paper: Cross-sensor domain adaptation for high-spatial resolution urban land-cover mapping: from airborne to spaceborne imagery
comingdowntoearth -> code for 2021 paper: Implementation of 'Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization'
Matching between acoustic and satellite images
MapRepair -> Deep Cadastre Maps Alignment and Temporal Inconsistencies Fix in Satellite Images
Compressive-Sensing-and-Deep-Learning-Framework -> Compressive Sensing is used as an initial guess to combine data from multiple sources, with LSTM used to refine the result
DeepSim -> code for paper: DeepSIM: GPS Spoofing Detection on UAVs using Satellite Imagery Matching
MHF-net -> code for 2019 paper: Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Remote_Sensing_Image_Fusion -> code for 2021 paper: Semi-Supervised Remote Sensing Image Fusion Using Multi-Scale Conditional Generative Adversarial network with Siamese Structure
CNNs for Multi-Source Remote Sensing Data Fusion -> code for 2021 paper: Single-stream CNN with Learnable Architecture for Multi-source Remote Sensing Data
Deep Generative Reflectance Fusion -> Achieving Landsat-like reflectance at any date by fusing Landsat and MODIS surface reflectance with deep generative models
IEEE_TGRS_MDL-RS -> code for 2021 paper: More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification
SSRNET -> code for 2022 paper: SSR-NET: Spatial-Spectral Reconstruction Network for Hyperspectral and Multispectral Image Fusion
cross-view-image-matching -> code for 2019 paper: Bridging the Domain Gap for Ground-to-Aerial Image Matching
CoF-MSMG-PCNN -> code for 2020 paper: Remote Sensing Image Fusion via Boundary Measured Dual-Channel PCNN in Multi-Scale Morphological Gradient Domain
robust_matching_network_on_remote_sensing_imagery_pytorch -> code for 2019 paper: A Robust Matching Network for Gradually Estimating Geometric Transformation on Remote Sensing Imagery
edcstfn -> code for 2019 paper: An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion
ganstfm -> code for 2021 paper: A Flexible Reference-Insensitive Spatiotemporal Fusion Model for Remote Sensing Images Using Conditional Generative Adversarial Network
CMAFF -> code for 2021 paper: Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery
SOLC -> code for 2022 paper: MCANet: A joint semantic segmentation framework of optical and SAR images for land use classification. Uses WHU-OPT-SAR-dataset
MFT -> code for 2022 paper: Multimodal Fusion Transformer for Remote Sensing Image Classification
ISPRS_S2FL -> code for 2021 paper: Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model

Terrain mapping, Disparity Estimation, Lidar, DEMs & NeRF

Measure surface contours & locate 3D points in space from 2D images. NeRF stands for Neural Radiance Fields and is the term used in deep learning communities to describe a model that generates views of complex 3D scenes based on a partial set of 2D images

Wikipedia DEM article and phase correlation article
Intro to depth from stereo
Map terrain from stereo images to produce a digital elevation model (DEM) -> high resolution & paired images required, typically 0.3 m, e.g. Worldview
Process of creating a DEM here and here.
ArcGIS can generate DEMs from stereo images
https://github.com/MISS3D/s2p -> produces elevation models from images taken by high resolution optical satellites -> demo code on https://gfacciol.github.io/IS18/
Automatic 3D Reconstruction from Multi-Date Satellite Images
Semi-global matching with neural networks
Predict the fate of glaciers
monodepth - Unsupervised single image depth prediction with CNNs
Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
Terrain and hydrological analysis based on LiDAR-derived digital elevation models (DEM) - Python package
Phase correlation in scikit-image
s2p -> a Python library and command line tool that implements a stereo pipeline which produces elevation models from images taken by high resolution optical satellites such as Pléiades, WorldView, QuickBird, Spot or Ikonos
The Mapbox API provides images and elevation maps, article here
Reconstructing 3D buildings from aerial LiDAR with Mask R-CNN
ResDepth -> A Deep Prior For 3D Reconstruction From High-resolution Satellite Images
overhead-geopose-challenge -> competition to build computer vision algorithms that can effectively model the height and pose of ground objects for monocular satellite images taken from oblique angles. Blog post MEET THE WINNERS OF THE OVERHEAD GEOPOSE CHALLENGE
cars -> a dedicated and open source 3D tool to produce Digital Surface Models from satellite imaging by photogrammetry. This Multiview stereo pipeline is intended for massive DSM production with a robust and performant design
ImageToDEM -> Generating Elevation Surface from a Single RGB Remotely Sensed Image Using a U-Net for generator and a PatchGAN for the discriminator
IMELE -> Building Height Estimation from Single-View Aerial Imagery
ridges -> deep semantic segmentation model for identifying ridges in topography
planet_tools -> Selection of imagery from Planet API for creation of stereo elevation models
SatelliteNeRF -> PyTorch-based Neural Radiance Fields adapted to satellite domain
SatelliteSfM -> A library for solving the satellite structure from motion problem
SatelliteSurfaceReconstruction -> 3D Surface Reconstruction From Multi-Date Satellite Images, ISPRS, 2021
son2sat -> A neural network coded in TensorFlow 1 that produces satellite images from acoustic images
aerial_mtl -> PyTorch implementation for multi-task learning with aerial images to learn both semantics and height from aerial image datasets; fuses RGB & lidar
ReKlaSat-3D -> 3D Reconstruction and Classification from Very High Resolution Satellite Imagery
M3Net -> A deep learning method for building height estimation using high-resolution multi-view imagery over urban areas
HMSM-Net -> code for 2022 paper: Hierarchical multi-scale matching network for disparity estimation of high-resolution satellite stereo images
StereoMatchingRemoteSensing -> code for 2021 paper: Dual-Scale Matching Network for Disparity Estimation of High-Resolution Remote Sensing Images
satnerf -> Learning Multi-View Satellite Photogrammetry With Transient Objects and Shadow Modeling Using RPC Cameras
SatMVS -> code for 2021 paper: Rational Polynomial Camera Model Warping for Deep Learning Based Satellite Multi-View Stereo Matching
ImpliCity -> reconstructs digital surface models (DSMs) from raw photogrammetric 3D point clouds and ortho-images with the help of an implicit neural 3D scene representation
WHU-Stereo -> a large-scale dataset for stereo matching of high-resolution satellite imagery & several deep learning methods for stereo matching. Methods include StereoNet, Pyramid Stereo Matching Network & HMSM-Net

Thermal Infrared

The World Needs (a lot) More Thermal Infrared Data from Space
IR2VI thermal-to-visible image translation framework based on GANs with code
The finest resolution urban outdoor heat exposure maps in major US cities -> urban microclimate modeling based on high resolution 3D urban models and meteorological data makes it possible to examine how people are exposed to heat stress at a fine spatio-temporal level.
Object_Classification_in_Thermal_Images -> classification accuracy was improved by adding the object size as a feature directly within the CNN
Thermal imaging with satellites blog post by Christoph Rieke
Object Detection on Thermal Images -> using YOLO-v3 and applied to a terrestrial dataset from FLIR, the article offers some usful insights into the model training
Fire alerts service by Descartes Labs
Background Invariant Classification on Infrared Imagery by Data Efficient Training and Reducing Bias in CNNs
CMAFF -> code for 2021 paper: Cross-Modality Attentive Feature Fusion for Object Detection in Multispectral Remote Sensing Imagery

SAR

awesome-sar -> A curated list of awesome Synthetic Aperture Radar (SAR) software, libraries, and resources
Removing speckle noise from Sentinel-1 SAR using a CNN
A dataset which is specifically made for deep learning on SAR and optical imagery is the SEN1-2 dataset, which contains corresponding patch pairs of Sentinel 1 (VV) and 2 (RGB) data. It is the largest manually curated dataset of S1 and S2 products, with corresponding labels for land use/land cover mapping, SAR-optical fusion, segmentation and classification tasks. Data: https://mediatum.ub.tum.de/1474000
so2sat on Tensorflow datasets -> So2Sat LCZ42 is a dataset consisting of co-registered synthetic aperture radar and multispectral optical image patches acquired by the Sentinel-1 and Sentinel-2 remote sensing satellites, and the corresponding local climate zones (LCZ) label. The dataset is distributed over 42 cities across different continents and cultural regions of the world.
You do not need clean images for SAR despeckling with deep learning -> How Speckle2Void learned to stop worrying and love the noise
PySAR - InSAR (Interferometric Synthetic Aperture Radar) timeseries analysis in python
Synthetic Aperture Radar (SAR) Analysis With Clarifai
Labeled SAR imagery dataset of ten geophysical phenomena from Sentinel-1 wave mode consists of more than 37,000 SAR vignettes divided into ten defined geophysical categories
Deep Learning and SAR Applications
Implementing an Ensemble Convolutional Neural Network on Sentinel-1 Synthetic Aperture Radar data and Sentinel-3 Radiometric data for the detecting of forest fires
s1_parking_occupancy -> Source code for PARKING OCCUPANCY ESTIMATION ON SENTINEL-1 IMAGES, ISPRS 2020
Experiments on Flood Segmentation on Sentinel-1 SAR Imagery with Cyclical Pseudo Labeling and Noisy Student Training
SpaceNet_SAR_Buildings_Solutions -> The winning solutions for the SpaceNet 6 Challenge
Mapping and monitoring of infrastructure in desert regions with Sentinel-1
xView3 is a competition to detect dark vessels using computer vision and global SAR satellite imagery. The-First-Place-Solution and second place solution. sar_vessel_detect
Winners of the STAC Overflow: Map Floodwater from Radar Imagery competition
deSpeckNet-TF-GEE -> implementation of the paper 'deSpeckNet: Generalizing Deep Learning Based SAR Image Despeckling'
cnn_sar_image_classification -> CNN for classifying SAR images of the Amazon Rainforest
s1_icetype_cnn -> Retrieve sea ice type from Sentinel-1 SAR with CNN
SARSeg -> pytorch code for the paper 'MP-ResNet: Multi-path Residual Network for the Semantic segmentation of PolSAR Images'
TGRS_DisOptNet -> Distilling Semantic Knowledge from Optical Images for Weather-independent Building Segmentation
SAR_CD_DDNet -> PyTorch implementation of Change Detection in Synthetic Aperture Radar Images Using a Dual Domain Network
SAR_CD_MS_CapsNet -> Code for the paper "Change Detection in SAR Images Based on Multiscale Capsule Network" IEEE GRSL 2021
anomaly-detection-in-SAR-imagery -> identify an unknown ship in docks using keras & retinanet
sar_transformer -> Transformer based SAR image despeckling, trained with synthetic imagery, with paper
SSDD ship detection dataset
Semantic segmentation of SAR images using a self supervised technique
Ship Detection on Remote Sensing Synthetic Aperture Radar Data -> based on the architectures of the Faster-RCNN and YOLOv5 networks
Target Recognition in SAR -> Identify Military Vehicles in Satellite Imagery with TensorFlow, with article
DSN -> code for 2020 paper: Deep SAR-Net: Learning objects from signals
SAR_denoising -> project on application of FFDNet to SAR images
sarCdUsingDeepTranscoding -> Details of a SAR to optical transcoder training. The generator of the transcoder is subsequently used for transfer learning in a change detection framework
cnninsar -> code for 2018 paper: CNN-Based InSAR Denoising and Coherence Metric
sar -> Despeckling Synthetic Aperture Radar Images using a Deep Residual CNN
GCBANet -> code for 2022 paper: A Global Context Boundary-Aware Network for SAR Ship Instance Segmentation
SAR_CD_GKSNet -> code for 2022 paper: Change Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network
pixel-wise-segmentation-of-sar -> code for 2020 paper: Pixel-Wise Segmentation of SAR Imagery Using Encoder-Decoder Network and Fully-Connected CRF
CenterSAR -> CenterNet for SAR ship detection based on Detectron2
SAR_Ship_detection_CFAR -> An improved two-parameter CFAR algorithm based on Rayleigh distribution and Mathematical Morphology for SAR ship detection
sar_snow_melt_timing -> notebooks and tools to identify snowmelt timing using timeseries analysis of backscatter of Sentinel-1 C-band SAR
Denoising radar satellite images using deep learning in Python -> Medium article on deepdespeckling

NVDI - vegetation index

Calculated via band math ndvi = np.true_divide((ir - r), (ir + r)) but challenging due to the size of the imagery
Example notebook local
Landsat data in cloud optimised (COG) format analysed for NVDI with medium article here.
Identifying Buildings in Satellite Images with Machine Learning and Quilt -> NDVI & edge detection via gaussian blur as features, fed to TPOT for training with labels from OpenStreetMap, modelled as a two class problem, “Buildings” and “Nature”
Seeing Through the Clouds - Predicting Vegetation Indices Using SAR
A walkthrough on calculating NDWI water index for flooded areas -> Derive zonal statistics from Sentinel 2 images using Rasterio and Geopandas
NDVI-Net -> code for 2020 paper: NDVI-Net: A fusion network for generating high-resolution normalized difference vegetation index in remote sensing
Awesome-Vegetation-Index
Remote-Sensing-Indices-Derivation-Tool -> Calculate spectral remote sensing indices from satellite imagery

General image quality

Convolutional autoencoder network can be employed to image denoising, read about this on the Keras blog
jitter-compensation -> Remote Sensing Image Jitter Detection and Compensation Using CNN
DeblurGANv2 -> Deblurring (Orders-of-Magnitude) Faster and Better
image-quality-assessment -> CNN to predict the aesthetic and technical quality of images
Convolutional autoencoder for image denoising -> keras guide
piq -> a collection of measures and metrics for image quality assessment
FFA-Net -> Feature Fusion Attention Network for Single Image Dehazing
DeepCalib -> A Deep Learning Approach for Automatic Intrinsic Calibration of Wide Field-of-View Cameras
PerceptualSimilarity -> LPIPS is a perceptual metric which aims to overcome the limitations of traditional metrics such as PSNR & SSIM, to better represent the features the human eye picks up on
Optical-RemoteSensing-Image-Resolution -> code for 2018 paper: Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration. Two applications: Gaussian image denoising and single image super-resolution

Neural nets in space

Processing on board a satellite allows less data to be downlinked. e.g. super-resolution image might take 8 images to generate, then a single image is downlinked. Other applications include cloud detection and collision avoidance.

Lockheed Martin and USC to Launch Jetson-Based Nanosatellite for Scientific Research Into Orbit - Aug 2020 - One app that will run on the GPU-accelerated satellite is SuperRes, an AI-based application developed by Lockheed Martin, that can automatically enhance the quality of an image.
Intel to place movidius in orbit to filter images of clouds at source - Oct 2020 - Getting rid of these images before they’re even transmitted means that the satellite can actually realize a bandwidth savings of up to 30%
Whilst not involving neural nets the PyCubed project gets a mention here as it is putting python on space hardware such as the V-R3x
WorldFloods will pioneer the detection of global flood events from space, launched on June 30, 2021. This paper describes the model which is run on Intel Movidius Myriad2 hardware capable of processing a 12 MP image in less than a minute
How AI and machine learning can support spacecraft docking with repo uwing Yolov3
exo-space -> startup with plans to release an AI hardware addon for satellites
Sony’s Spresense microcontroller board is going to space -> vision applications include cloud detection, more details here
Ororatech Early Detection of Wildfires From Space -> OroraTech is launching its own AI nanosatellites with the NVIDIA Jetson Xavier NX system onboard
Palantir Edge AI in Space -> using NVIDIA Jetson for ship/aircraft/cloud detection & land cover segmentation

ML best practice

This section includes tips and ideas I have picked up from other practitioners including ai-fast-track, FraPochetti & the IceVision community

Almost all imagery data on the internet is in RGB format, and common techniques designed for working with this 3 band imagery may fail or need significant adaptation to work with multiband data (e.g. 13-band Sentinel 2)
In general, classification and object detection models are created using transfer learning, where the majority of the weights are not updated in training but have been pre computed using standard vision datasets such as ImageNet
Since satellite images are typically very large, it is common to tile them before processing. Alternatively checkout Fully Convolutional Image Classification on Arbitrary Sized Image -> TLDR replace the fully-connected layer with a convolution-layer
Where you have small sample sizes, e.g. for a small object class which may be under represented in your training dataset, use image augmentation
In general, larger models will outperform smaller models, particularly on challenging tasks such as detecting small objetcs
If model performance in unsatisfactory, try to increase your dataset size before switching to another model architecture
In training, whenever possible increase the batch size, as small batch sizes produce poor normalization statistics
The vast majority of the literature uses supervised learning with the requirement for large volumes of annotated data, which is a bottleneck to development and deployment. We are just starting to see self-supervised approaches applied to remote sensing data
4-ways-to-improve-class-imbalance discusses the pros and cons of several rebalancing techniques, applied to an aerial dataset. Reason to read: models can reach an accuracy ceiling where majority classes are easily predicted but minority classes poorly predicted. Overall model accuracy may not improve until steps are taken to account for class imbalance.
For general guidance on dataset size see this issue
Read A Recipe for Training Neural Networks by Andrej Karpathy
Seven steps towards a satellite imagery dataset
Implementing Transfer Learning from RGB to Multi-channel Imagery -> takes a resnet50 model pre-trained on an input of 224x224 pixels with 3 channels (RGB) and updates it for a new input of 480x400 pixels and 15 channels (12 new + RGB) using keras
How to implement augmentations for Multispectral Satellite Images Segmentation using Fastai-v2 and Albumentations
Principal Component Analysis: In-depth understanding through image visualization applied to Landsat TM images, with repo
Leveraging Geolocation Data for Machine Learning: Essential Techniques -> A Gentle Guide to Feature Engineering and Visualization with Geospatial data, in Plain English
3 Tips to Optimize Your Machine Learning Project for Data Labeling
Image Classification Labeling: Single Class versus Multiple Class Projects
Labeling Satellite Imagery for Machine Learning
Image Augmentations for Aerial Datasets
Leveraging satellite imagery for machine learning computer vision applications
Best Practices for Preparing and Augmenting Image Data for CNNs
Using TensorBoard While Training Land Cover Models with Satellite Imagery
An Overview of Model Compression Techniques for Deep Learning in Space
Visualise Embeddings with Tensorboard -> also checkout the Tensorflow Embedding Projector
Introduction to Satellite Image Augmentation with Generative Adversarial Networks - video
Use Gradio and W&B together to monitor training and view predictions
Every important satellite imagery analysis project is challenging, but here are ten straightforward steps to get started
Challenges with SpaceNet 4 off-nadir satellite imagery: Look angle and target azimuth angle -> building prediction in images taken at nearly identical look angles — for example, 29 and 30 degrees — produced radically different performance scores.
How not to test your deep learning algorithm? - bad ideas to avoid
AI products and remote sensing: yes, it is hard and yes, you need a good infra -> advice on building an in-house data annotation service
Boosting object detection performance through ensembling on satellite imagery
How to use deep learning on satellite imagery — Playing with the loss function
On the importance of proper data handling
Generate SSD anchor box aspect ratios using k-means clustering -> tutorial showing how to discover a set of aspect ratios that are custom-fit for your dataset, applied to tensorflow object detection
Transfer Learning on Greyscale Images: How to Fine-Tune Pretrained Models on Black-and-White Datasets
How to create a DataBlock for Multispectral Satellite Image Segmentation with the Fastai
A comprehensive list of ML and AI acronyms and abbreviations
Finding an optimal number of “K” classes for unsupervised classification on Remote Sensing Data -> i.e 'elbow' method
Supplement your training data with 'negative' examples which are created through random selection of regions of the image that contain no objects of interest, read Setting a Foundation for Machine Learning
The law of diminishing returns often applies to dataset size, read Quantifying the Effects of Resolution on Image Classification Accuracy
Implementing Transfer Learning from RGB to Multi-channel Imagery -> Medium article which discusses how to convert a model trained on 3 channels to more channels, adding an additional 12 channels to the original 3 channel RGB image, uses Keras
satellite-segmentation-pytorch -> explores a wide variety of image augmentations to increase training dataset size
Quantifying uncertainty in deep learning systems
How to create a custom Dataset / Loader in PyTorch, from Scratch, for multi-band Satellite Images Dataset from Kaggle -> uses the 38-Cloud dataset

Metrics

A number of metrics are common to all model types (but can have slightly different meanings in contexts such as object detection), whilst other metrics are very specific to particular classes of model. The correct choice of metric is particularly critical for imbalanced dataset problems, e.g. object detection

TP = true positive, FP = false positive, TN = true negative, FN = false negative
Precision is the % of correct positive predictions, calculated as precision = TP/(TP+FP)
Recall or true positive rate (TPR), is the % of true positives captured by the model, calculated as recall = TP/(TP+FN). Note that FN is not possible in object detection, so recall is not appropriate.
The F1 score (also called the F-score or the F-measure) is the harmonic mean of precision and recall, calculated as F1 = 2*(precision * recall)/(precision + recall). It conveys the balance between the precision and the recall. Ref
The false positive rate (FPR), calculated as FPR = FP/(FP+TN) is often plotted against recall/TPR in an ROC curve which shows how the TPR/FPR tradeoff varies with classification threshold. Lowering the classification threshold returns more true positives, but also more false positives. Note that since FN is not possible in object detection, ROC curves are not appropriate.
Precision-vs-recall curves visualise the tradeoff between making false positives and false negatives
Accuracy is the most commonly used metric in 'real life' but can be a highly misleading metric for imbalanced data sets.
IoU is an object detection specific metric, being the average intersect over union of prediction and ground truth bounding boxes for a given confidence threshold
[email protected] is another object detection specific metric, being the mean value of the average precision for each class. @0.5 sets a threshold for how much of the predicted bounding box overlaps the ground truth bounding box, i.e. "minimum 50% overlap"
For more comprehensive definitions checkout Object-Detection-Metrics

Datasets

This section contains a short list of datasets relevant to deep learning, particularly those which come up regularly in the literature. Warning satellite image files can be LARGE, and even a small datasets may comprise 50GB+ of imagery

Lists of datasets

Earth Observation Database
awesome-satellite-imagery-datasets
Awesome_Satellite_Benchmark_Datasets
Callisto-Dataset-Collection -> datasets that use Copernicus/sentinel data

Sentinel

As part of the EU Copernicus program, multiple Sentinel satellites are capturing imagery -> see wikipedia.
13 bands, Spatial resolution of 10 m, 20 m and 60 m, 290 km swath, the temporal resolution is 5 days
awesome-sentinel -> a curated list of awesome tools, tutorials and APIs related to data from the Copernicus Sentinel Satellites.
Sentinel-2 Cloud-Optimized GeoTIFFs and Sentinel-2 L2A 120m Mosaic
Open access data on GCP
Paid access to Sentinel & Landsat data via sentinel-hub and python-api
Example loading sentinel data in a notebook
so2sat on Tensorflow datasets - So2Sat LCZ42 is a dataset consisting of co-registered synthetic aperture radar and multispectral optical image patches acquired by the Sentinel-1 and Sentinel-2 remote sensing satellites, and the corresponding local climate zones (LCZ) label. The dataset is distributed over 42 cities across different continents and cultural regions of the world.
BigEarthNet - The BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of 590,326 Sentinel-2 image patches. The image patch size on the ground is 1.2 x 1.2 km with variable image size depending on the channel resolution. This is a multi-label dataset with 43 imbalanced labels. Also available in torchgeo
Jupyter Notebooks for working with Sentinel-5P Level 2 data stored on S3. The data can be browsed here
Sentinel NetCDF data
Analyzing Sentinel-2 satellite data in Python with Keras
Xarray backend to Copernicus Sentinel-1 satellite data products
SEN2VENµS -> a dataset for the training of Sentinel-2 super-resolution algorithms
SEN12MS -> A Curated Dataset of Georeferenced Multi-spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion. Checkout SEN12MS toolbox and many referenced uses on paperswithcode.com
Sen4AgriNet -> A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning, with website and models
earthspy -> Monitor and study any place on Earth and in Near Real-Time (NRT) using the Sentinel Hub services developed by the EO research team at Sinergise
Space2Ground -> dataset with Space (Sentinel-1/2) and Ground (street-level images) components, annotated with crop-type labels for agriculture monitoring.
sentinel2tools -> downloading & basic processing of Sentinel 2 imagesry. Read Sentinel2tools: simple lib for downloading Sentinel-2 satellite images

Landsat

Long running US program -> see Wikipedia
8 bands, 15 to 60 meters, 185km swath, the temporal resolution is 16 days
Landsat 4, 5, 7, and 8 imagery on Google, see the GCP bucket here, with Landsat 8 imagery in COG format analysed in this notebook
Landsat 8 imagery on AWS, with many tutorials and tools listed
https://github.com/kylebarron/landsat-mosaic-latest -> Auto-updating cloudless Landsat 8 mosaic from AWS SNS notifications
Visualise landsat imagery using Datashader
Landsat-mosaic-tiler -> This repo hosts all the code for landsatlive.live website and APIs.

Maxar

Satellites owned by Maxar (formerly DigitalGlobe) include GeoEye-1, WorldView-2, 3 & 4
Open Data images for humanitarian response
Maxar ARD (COG plus data masks, with STAC) sample data in S3
Dataset on AWS -> see this getting started notebook and this notebook on the off-Nadir dataset
cloud_optimized_geotif here used in the 3D modelling notebook here.
WorldView cloud optimized geotiffs used in the 3D modelling notebook here.
For more Worldview imagery see Kaggle DSTL competition.

Planet

Planet’s high-resolution, analysis-ready mosaics of the world’s tropics, supported through Norway’s International Climate & Forests Initiative. BBC coverage
Planet have made imagery available via kaggle competitions

UC Merced

Land use classification dataset with 21 classes and 100 RGB TIFF images for each class
Each image measures 256x256 pixels with a pixel resolution of 1 foot
http://weegee.vision.ucmerced.edu/datasets/landuse.html
Available as a Tensorflow dataset -> https://www.tensorflow.org/datasets/catalog/uc_merced
Also available as a multi-label dataset
Read Vision Transformers for Remote Sensing Image Classification where a Vision Transformer classifier achieves 98.49% classification accuracy on Merced

EuroSAT

Land use classification dataset of Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples. Available in RGB and 13 band versions
EuroSAT: Land Use and Land Cover Classification with Sentinel-2 -> publication where a CNN achieves a classification accuracy 98.57%
Repos using fastai here and here
evolved_channel_selection -> explores the trade off between mixed resolutions and whether to use a channel at all, with repo
RGB version available as dataset in pytorch with the 13 band version in torchgeo. Checkout the tutorial on data augmentation with this dataset
RGB and 13 band versions in tensorflow

PatternNet

Land use classification dataset with 38 classes and 800 RGB JPG images for each class
https://sites.google.com/view/zhouwx/dataset?authuser=0
Publication: PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval

Million-AID

https://captain-whu.github.io/DiRS/
a new large-scale benchmark dataset containing million instances for RS scene classification
51 scene categories organized by the hierarchical category
Pretrained models
Also see AID, AID-Multilabel-Dataset & DFC15-multilabel-dataset

DIOR object detection dataset

https://gcheng-nwpu.github.io/
https://arxiv.org/abs/1909.00133
"DIOR" is a large-scale benchmark dataset for object detection in optical remote sensing images, which consists of 23,463 images and 192,518 object instances annotated with horizontal bounding boxes
ors-detection -> Object Detection on the DIOR dataset using YOLOv3
dior_detect -> benchmarks for object detection on DIOR dataset
Tools -> for dealing with the DIOR

Multiscene

https://multiscene.github.io/ & https://github.com/Hua-YS/Multi-Scene-Recognition
MultiScene dataset aims at two tasks: Developing algorithms for multi-scene recognition & Network learning with noisy labels

FAIR1M object detection dataset

FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery
Download at gaofen-challenge.com
2020Gaofen -> 2020 Gaofen Challenge data, baselines, and metrics

DOTA object detection dataset

https://captain-whu.github.io/DOTA/index.html
A Large-Scale Benchmark and Challenges for Object Detection in Aerial Images
DOTA_devkit for loading dataset
Arxiv paper
Pretrained models in mmrotate
DOTA2VOCtools -> dataset split and transform to voc format

HRSC RGB ship object detection dataset

SAR Ship Detection Dataset (SSDD)

SAR Aircraft Detection Dataset

https://github.com/hust-rslab/SAR-aircraft-data
2966 nonoverlapped 224×224 slices are collected with 7835 aircraft targets

xView object detection dataset

http://xviewdataset.org/
One million annotated objects on 30cm imagery
The XView Dataset and Baseline Results blog post by Picterra
Databricks tutorial demonstrating inference of xView images and using SQL to generate meaningful insights

Vehicle Detection in Aerial Imagery (VEDAI)

https://downloads.greyc.fr/vedai/
pytorch-vedai -> object detection on the VEDAI dataset: Vehicle Detection in Aerial Imagery

Cars Overhead With Context (COWC)

http://gdo152.ucllnl.org/cowc/
https://github.com/LLNL/cowc
Large set of annotated cars from overhead
Established baseline for detection and counting tasks

AI-TOD - tiny object detection

https://github.com/jwwangchn/AI-TOD
The mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than other datasets
NWD -> code for 2021 paper: A Normalized Gaussian Wasserstein Distance for Tiny Object Detection. Uses AI-TOD dataset

Counting from Sky

A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method
https://github.com/gaoguangshuai/Counting-from-Sky-A-Large-scale-Dataset-for-Remote-Sensing-Object-Counting-and-A-Benchmark-Method

AIRS (Aerial Imagery for Roof Segmentation)

https://www.airs-dataset.com
Public dataset for roof segmentation from very-high-resolution aerial imagery (7.5cm)
AIRS dataset covers almost the full area of Christchurch, the largest city in the South Island of New Zealand.
Also on Kaggle
Rooftop-Instance-Segmentation -> VGG-16, Instance Segmentation, uses the Airs dataset

Inria building/not building segmentation dataset

https://project.inria.fr/aerialimagelabeling/contest/
RGB GeoTIFF at spatial resolution of 0.3 m
Data covering Austin, Chicago, Kitsap County, Western & Easter Tyrol, Innsbruck, San Francisco & Vienna
SemSegBuildings -> Project using fast.ai framework for semantic segmentation on Inria building segmentation dataset
UNet_keras_for_RSimage -> keras code for binary semantic segmentation

AICrowd Mapping Challenge \building segmentation dataset

Dataset release as part of the mapping-challenge
300x300 pixel RGB images with annotations in COCO format
Imagery appears to be global but with significant fraction from North America
Winning solution published by neptune.ai here, achieved precision 0.943 and recall 0.954 using Unet with Resnet.
mappingchallenge -> YOLOv5 applied to the AICrowd Mapping Challenge dataset

BONAI - building footprint dataset

https://github.com/jwwangchn/BONAI
BONAI (Buildings in Off-Nadir Aerial Images) is a dataset for building footprint extraction (BFE) in off-nadir aerial images

GID15 large scale semantic segmentation dataset

https://captain-whu.github.io/GID15/

LEVIR-CD building change detection dataset

https://justchenhao.github.io/LEVIR/
FCCDN_pytorch -> pytorch implemention of FCCDN for change detection task
RSICC -> the Remote Sensing Image Change Captioning dataset uses LEVIR-CD imagery

ISPRS

https://www.isprs.org/education/benchmarks/UrbanSemLab/2d-sem-label-potsdam.aspx
Semantic segmentation dataset
38 patches of 6000x6000 pixels, each consisting of a true orthophoto (TOP) extracted from a larger TOP mosaic, and a DSM. Resolution 5 cm

iSAID

https://captain-whu.github.io/iSAID/dataset.html
A Large-scale Dataset for Instance Segmentation in Aerial Images

SpaceNet

spacenet.ai is an online hub for data, challenges, algorithms, and tools. Note that CosmiQ Ended its Leadership of SpaceNet, handing over the reigns to Maxar
SpaceNet ran a series consisting of seven challenges with datasets and utilities provided. Challenges covered (1&2) building segmentation, (3) road segmentation, (4) off-nadir buildings, (5) road network extraction, (6)multi-senor mapping, (7) multi-temporal urban change
Building datasets covered a number of cities including: Rio, Paris, Vegas, Shanghai, Khartoum, Atlana, Moscow, Mumbai & Rotterdam
The SpaceNet 7 Multi-Temporal Urban Development Challenge: Dataset Release
spacenet-three-topcoder solution
official utilities -> Packages intended to assist in the preprocessing of SpaceNet satellite imagery data corpus to a format that is consumable by machine learning algorithms
andraugust spacenet-utils -> Display geotiff image with building-polygon overlay & label buildings using kNN on the pixel spectra
Spacenet-Building-Detection -> uses keras and Spacenet 1 dataset

WorldStrat Dataset

https://github.com/worldstrat/worldstrat
Nearly 10,000 km² of free high-resolution satellite imagery of unique locations which ensure stratified representation of all types of land-use across the world: from agriculture to ice caps, from forests to multiple urbanization densities.
Each high-resolution image (1.5 m/pixel) comes with multiple temporally-matched low-resolution images from the freely accessible lower-resolution Sentinel-2 satellites (10 m/pixel)
Several super-resolution benchmark models trained on it

Tensorflow datasets

resisc45 -> RESISC45 dataset is a publicly available benchmark for Remote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class.
eurosat -> EuroSAT dataset is based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.
BigEarthNet -> a large-scale Sentinel-2 land use classification dataset, consisting of 590,326 Sentinel-2 image patches. The image patch size on the ground is 1.2 x 1.2 km with variable image size depending on the channel resolution. This is a multi-label dataset with 43 imbalanced labels. Official website includes version of the dataset with Sentinel 1 & 2 chips
so2sat -> a dataset consisting of co-registered synthetic aperture radar and multispectral optical image patches acquired by Sentinel 1 & 2

AWS datasets

Earth on AWS is the AWS equivalent of Google Earth Engine
Currently 36 satellite datasets on the Registry of Open Data on AWS

Microsoft

US Building Footprints -> building footprints in all 50 US states, GeoJSON format, generated using semantic segmentation. Also Australia, Canadian, Uganda-Tanzania, Kenya-Nigeria and GlobalMLBuildingFootprints are available. Checkout RasterizingBuildingFootprints to convert vector shapefiles to raster layers
Microsoft Planetary Computer is a Dask-Gateway enabled JupyterHub deployment focused on supporting scalable geospatial analysis, source repo
landcover-orinoquia -> Land cover mapping of the Orinoquía region in Colombia, in collaboration with Wildlife Conservation Society Colombia. An #AIforEarth project
RoadDetections dataset by Microsoft

Google Earth Engine (GEE)

Since there is a whole community around GEE I will not reproduce it here but list very select references. Get started at https://developers.google.com/earth-engine/

Various imagery and climate datasets, including Landsat & Sentinel imagery
Supports large scale processing with classical algorithms, e.g. clustering for land use. For deep learning, you export datasets from GEE as tfrecords, train on your preferred GPU platform, then upload inference results back to GEE
awesome-google-earth-engine
Awesome-GEE
awesome-earth-engine-apps
How to Use Google Earth Engine and Python API to Export Images to Roboflow -> to acquire training data
ee-fastapi is a simple FastAPI web application for performing flood detection using Google Earth Engine in the backend.
How to Download High-Resolution Satellite Data for Anywhere on Earth
wxee -> Export data from GEE to xarray using wxee then train with pytorch or tensorflow models. Useful since GEE only suports tfrecord export natively

Radiant Earth

https://www.radiant.earth/
Datasets and also models on https://mlhub.earth/

Image captioning datasets

RSICD -> 10921 images with five sentences descriptions per image. Used in Fine tuning CLIP with Remote Sensing (Satellite) images and captions, models at this repo
RSICC -> the Remote Sensing Image Change Captioning dataset contains 10077 pairs of bi-temporal remote sensing images and 50385 sentences describing the differences between images. Uses LEVIR-CD imagery

Weather Datasets

UK metoffice -> https://www.metoffice.gov.uk/datapoint
NASA (make request and emailed when ready) -> https://search.earthdata.nasa.gov
NOAA (requires BigQuery) -> https://www.kaggle.com/noaa/goes16/home
Time series weather data for several US cities -> https://www.kaggle.com/selfishgene/historical-hourly-weather-data
DeepWeather -> improve weather forecasting accuracy by analyzing satellite images

Forest datasets

awesome-forests -> A curated list of ground-truth forest datasets for the machine learning and forestry community
ReforesTree -> A dataset for estimating tropical forest biomass based on drone and field data

Geospatial datasets

Resource Watch provides a wide range of geospatial datasets and a UI to visualise them

Time series & change detection datasets

BreizhCrops -> A Time Series Dataset for Crop Type Mapping
The SeCo dataset contains image patches from Sentinel-2 tiles captured at different timestamps at each geographical location. Download SeCo here
Onera Satellite Change Detection Dataset comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018
SYSU-CD -> The dataset contains 20000 pairs of 0.5-m aerial images of size 256×256 taken between the years 2007 and 2014 in Hong Kong

DEM (digital elevation maps)

Shuttle Radar Topography Mission, search online at usgs.gov
Copernicus Digital Elevation Model (DEM) on S3, represents the surface of the Earth including buildings, infrastructure and vegetation. Data is provided as Cloud Optimized GeoTIFFs. link

UAV & Drone datasets

Many on https://www.visualdata.io
AU-AIR dataset -> a multi-modal UAV dataset for object detection.
ERA -> A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos.
Aerial Maritime Drone Dataset
RetinaNet for pedestrian detection
Aerial Maritime Drone Dataset
EmergencyNet -> identify fire and other emergencies from a drone
OpenDroneMap -> generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images.
Dataset of thermal and visible aerial images for multi-modal and multi-spectral image registration and fusion -> The dataset consists of 30 visible images and their metadata, 80 thermal images and their metadata, and a visible georeferenced orthoimage.
BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos -> TIR videos of humans and animals with several challenging scenarios like scale variations, background clutter due to thermal reflections, large camera rotations, and motion blur
ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
DroneVehicle -> Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning
UAVOD10 -> 10 class of objects at 15 cm resolution. Classes are; building, ship, vehicle, prefabricated house, well, cable tower, pool, landslide, cultivation mesh cage, and quarry.
Busy-parking-lot-dataset---vehicle-detection-in-UAV-video

Other datasets

land-use-land-cover-datasets
EORSSD-dataset -> Extended Optical Remote Sensing Saliency Detection (EORSSD) Dataset
RSD46-WHU -> 46 scene classes for image classification, free for education, research and commercial use
RSOD-Dataset -> dataset for object detection in PASCAL VOC format. Aircraft, playgrounds, overpasses & oiltanks
VHR-10_dataset_coco -> Object detection and instance segmentation dataset based on NWPU VHR-10 dataset. RGB & SAR
HRSID -> high resolution sar images dataset for ship detection, semantic segmentation, and instance segmentation tasks
MAR20 -> Military Aircraft Recognition dataset
RSSCN7 -> Dataset of the article “Deep Learning Based Feature Selection for Remote Sensing Scene Classification”
Sewage-Treatment-Plant-Dataset -> object detection
TGRS-HRRSD-Dataset -> High Resolution Remote Sensing Detection (HRRSD)
MUSIC4HA -> MUltiband Satellite Imagery for object Classification (MUSIC) to detect Hot Area
MUSIC4GC -> MUltiband Satellite Imagery for object Classification (MUSIC) to detect Golf Course
MUSIC4P3 -> MUltiband Satellite Imagery for object Classification (MUSIC) to detect Photovoltaic Power Plants (solar panels)
ABCDdataset -> damage detection dataset to identify whether buildings have been washed-away by tsunami
OGST -> Oil and Gas Tank Dataset
LS-SSDD-v1.0-OPEN -> Large-Scale SAR Ship Detection Dataset
S2Looking -> A Satellite Side-Looking Dataset for Building Change Detection, paper
Zurich Summer Dataset -> Semantic segmentation of urban scenes
AISD -> Aerial Imagery dataset for Shadow Detection
LEVIR-Ship -> a dataset for tiny ship detection under medium-resolution remote sensing images
Awesome-Remote-Sensing-Relative-Radiometric-Normalization-Datasets
SearchAndRescueNet -> Satellite Imagery for Search And Rescue Dataset, with example Faster R-CNN model
geonrw -> orthorectified aerial photographs, LiDAR derived digital elevation models and segmentation maps with 10 classes. With repo
Thermal power plans dataset
University1652-Baseline -> A Multi-view Multi-source Benchmark for Drone-based Geo-localization
benchmark_ISPRS2021 -> A new stereo dense matching benchmark dataset for deep learning
WHU-SEN-City -> A paired SAR-to-optical image translation dataset which covers 34 big cities of China
SAR_vehicle_detection_dataset -> 104 SAR images for vehicle detection, collected from Sandia MiniSAR/FARAD SAR images and MSTAR images
ERA-DATASET -> A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
SSL4EO-S12 -> a large-scale dataset for self-supervised learning in Earth observation
UBC-dataset -> a dataset for building detection and classification from very high-resolution satellite imagery with the focus on object-level interpretation of individual buildings
AIR-CD -> a challenging cloud detection data set called AIR-CD, with higher spatial resolution and more representative landcover types
AIR-PolSAR-Seg -> a challenging PolSAR terrain segmentation dataset
HRC_WHU -> High-Resolution Cloud Detection Dataset comprising 150 RGB images and a resolution varying from 0.5 to 15 m in different global regions
AeroRIT -> A New Scene for Hyperspectral Image Analysis
Building_Dataset -> High-speed Rail Line Building Dataset Display
Haiming-Z/MtS-WH-reference-map -> a reference map for change detection based on MtS-WH
MtS-WH-Dataset -> Multi-temporal Scene WuHan (MtS-WH) Dataset

Kaggle

Kaggle hosts over > 200 satellite image datasets, search results here. The kaggle blog is an interesting read.

Kaggle - Amazon from space - classification challenge

https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data
3-5 meter resolution GeoTIFF images from planet Dove satellite constellation
12 classes including - cloudy, primary + waterway etc
1st place winner interview - used 11 custom CNN
FastAI Multi-label image classification
Multi-Label Classification of Satellite Photos of the Amazon Rainforest
Understanding the Amazon Rainforest with Multi-Label Classification + VGG-19, Inceptionv3, AlexNet & Transfer Learning
amazon-classifier -> compares random forest with CNN
multilabel-classification -> compares various CNN architecutres
Planet-Amazon-Kaggle -> uses fast.ai
deforestation_deep_learning
Track-Human-Footprint-in-Amazon-using-Deep-Learning
Amazon-Rainforest-CNN -> uses a 3-layer CNN in Tensorflow
rainforest-tagging -> Convolutional Neural Net and Recurrent Neural Net in Tensorflow for satellite images multi-label classification

Kaggle - DSTL segmentation challenge

https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection
Rating - medium, many good examples (see the Discussion as well as kernels), but as this competition was run a couple of years ago many examples use python 2
WorldView 3 - 45 satellite images covering 1km x 1km in both 3 (i.e. RGB) and 16-band (400nm - SWIR) images
10 Labelled classes include - Buildings, Road, Trees, Crops, Waterway, Vehicles
Interview with 1st place winner who used segmentation networks - 40+ models, each tweaked for particular target (e.g. roads, trees)
ZF_UNET_224_Pretrained_Model 2nd place solution ->
3rd place soluton -> which explored pansharpening & calculating reflectance indices, with arxiv paper
Deepsense 4th place solution
Entry by lopuhin using UNet with batch-normalization
Multi-class semantic segmentation of satellite images using U-Net using DSTL dataset, tensorflow 1 & python 2.7. Accompanying article
Deep-Satellite-Image-Segmentation
Dstl-Satellite-Imagery-Feature-Detection-Improved
Satellite-imagery-feature-detection
Satellite_Image_Classification -> using XGBoost and ensemble classification methods
Unet-for-Satellite
building-segmentation -> TensorFlow U-Net implementation trained to segment buildings in satellite imagery

Kaggle - DeepSat land cover classification

https://www.kaggle.com/datasets/crawford/deepsat-sat4 & https://www.kaggle.com/datasets/crawford/deepsat-sat6
DeepSat-Kaggle -> uses Julia
deepsat-aws-emr-pyspark -> Using PySpark for Image Classification on Satellite Imagery of Agricultural Terrains

Kaggle - Airbus ship detection challenge

https://www.kaggle.com/c/airbus-ship-detection/overview
Rating - medium, most solutions using deep-learning, many kernels, good example kernel
I believe there was a problem with this dataset, which led to many complaints that the competition was ruined
Deep Learning for Ship Detection and Segmentation -> treated as instance segmentation problem, with notebook
Lessons Learned from Kaggle’s Airbus Challenge
Airbus-Ship-Detection -> This solution scored 139 out of 884 for the competition, combines ResNeXt50 based classifier and a U-net segmentation model
Ship-Detection-Project -> uses Mask R-CNN and UNet model
Airbus_SDC
Airbus_SDC_dup -> Project focused on detecting duplicate regions of overlapping satellite imagery. Applied to Airbus ship detection dataset
airbus-ship-detection -> CNN with REST API

Kaggle - Shipsnet classification dataset

https://www.kaggle.com/rhammell/ships-in-satellite-imagery -> Classify ships in San Franciso Bay using Planet satellite imagery
4000 80x80 RGB images labeled with either a "ship" or "no-ship" classification, 3 meter pixel size
shipsnet-detector -> Detect container ships in Planet imagery using machine learning

Kaggle - Ships in Google Earth

https://www.kaggle.com/tomluther/ships-in-google-earth
794 jpegs showing various sized ships in satellite imagery, annotations in Pascal VOC format for object detection models
kaggle-ships-in-Google-Earth-yolov5

Kaggle - Ships in San Franciso Bay

https://www.kaggle.com/datasets/rhammell/ships-in-satellite-imagery
4000 80x80 RGB images labeled with either a "ship" or "no-ship" classification, provided by Planet
DeepLearningShipDetection
Ship-Detection-Using-Satellite-Imagery

Kaggle - Swimming pool and car detection using satellite imagery

https://www.kaggle.com/kbhartiya83/swimming-pool-and-car-detection
3750 satellite images of residential areas with annotation data for swimming pools and cars
Object detection on Satellite Imagery using RetinaNet

Kaggle - Planesnet classification dataset

https://www.kaggle.com/rhammell/planesnet -> Detect aircraft in Planet satellite image chips
20x20 RGB images, the "plane" class includes 8000 images and the "no-plane" class includes 24000 images
Dataset repo and planesnet-detector demonstrates a small CNN classifier on this dataset
ergo-planes-detector -> An ergo based project that relies on a convolutional neural network to detect airplanes from satellite imagery, uses the PlanesNet dataset
Using AWS SageMaker/PlanesNet to process Satellite Imagery
Airplane-in-Planet-Image -> pytorch model

Kaggle - CGI Planes in Satellite Imagery w/ BBoxes

https://www.kaggle.com/datasets/aceofspades914/cgi-planes-in-satellite-imagery-w-bboxes
500 computer generated satellite images of planes
Faster RCNN to detect airplanes
aircraft-detection-from-satellite-images-yolov3

Kaggle - Draper challenge to place images in order of time

https://www.kaggle.com/c/draper-satellite-image-chronology/data
Rating - hard. Not many useful kernels.
Images are grouped into sets of five, each of which have the same setId. Each image in a set was taken on a different day (but not necessarily at the same time each day). The images for each set cover approximately the same area but are not exactly aligned.
Kaggle interviews for entrants who used XGBOOST and a hybrid human/ML approach
deep-cnn-sat-image-time-series -> uses LSTM

Kaggle - Dubai segmentation

https://www.kaggle.com/humansintheloop/semantic-segmentation-of-aerial-imagery
72 satellite images of Dubai, the UAE, and is segmented into 6 classes
dubai-satellite-imagery-segmentation -> due to the small dataset, image augmentation was used
U-Net for Semantic Segmentation on Unbalanced Aerial Imagery -> using the Dubai dataset
Multiclass-semantic-segmentation-in-satallite-images -> uses keras
Semantic-Segmentation-using-U-Net -> uses keras
unet_satelite_image_segmentation

Kaggle - Massachusetts Roads & Buildings Datasets - segmentation

https://www.kaggle.com/datasets/balraj98/massachusetts-roads-dataset
https://www.kaggle.com/datasets/balraj98/massachusetts-buildings-dataset
Official published dataset
Road_seg_dataset -> subset of the roads dataset containing only 200 images and masks
Road and Building Semantic Segmentation in Satellite Imagery uses U-Net on the Massachusetts Roads Dataset & keras
Semantic-segmentation repo by fuweifu-vtoo -> uses pytorch and the Massachusetts Buildings & Roads Datasets
ssai-cnn -> This is an implementation of Volodymyr Mnih's dissertation methods on his Massachusetts road & building dataset
building-footprint-segmentation -> pip installable library to train building footprint segmentation on satellite and aerial imagery, applied to Massachusetts Buildings Dataset and Inria Aerial Image Labeling Dataset
Road detection using semantic segmentation and albumentations for data augmention using the Massachusetts Roads Dataset, U-net & Keras
Image-Segmentation) -> using Massachusetts Road dataset and fast.ai

Kaggle - Deepsat classification challenge

Not satellite but airborne imagery. Each sample image is 28x28 pixels and consists of 4 bands - red, green, blue and near infrared. The training and test labels are one-hot encoded 1x6 vectors. Each image patch is size normalized to 28x28 pixels. Data in .mat Matlab format. JPEG?

Sat4 500,000 image patches covering four broad land cover classes - barren land, trees, grassland and a class that consists of all land cover classes other than the above three
Sat6 405,000 image patches each of size 28x28 and covering 6 landcover classes - barren land, trees, grassland, roads, buildings and water bodies.

Kaggle - High resolution ship collections 2016 (HRSC2016)

https://www.kaggle.com/guofeng/hrsc2016
Ship images harvested from Google Earth
HRSC2016_SOTA -> Fair comparison of different algorithms on the HRSC2016 dataset

Kaggle - SWIM-Ship Wake Imagery Mass

https://www.kaggle.com/datasets/lilitopia/swimship-wake-imagery-mass
An optical ship wake detection benchmark dataset built for deep learning
WakeNet -> A CNN-based optical image ship wake detector, code for 2021 paper: Rethinking Automatic Ship Wake Detection: State-of-the-Art CNN-based Wake Detection via Optical Images

Kaggle - Understanding Clouds from Satellite Images

In this challenge, you will build a model to classify cloud organization patterns from satellite images.

Kaggle - 38-Cloud Cloud Segmentation

https://www.kaggle.com/datasets/sorour/38cloud-cloud-segmentation-in-satellite-images
Contains 38 Landsat 8 images and manually extracted pixel-level ground truths
How to create a custom Dataset / Loader in PyTorch, from Scratch, for multi-band Satellite Images Dataset from Kaggle
Cloud-Net: A semantic segmentation CNN for cloud detection -> an end-to-end cloud detection algorithm for Landsat 8 imagery, trained on 38-Cloud Training Set
Segmentation of Clouds in Satellite Images Using Deep Learning -> semantic segmentation using a Unet on the Kaggle 38-Cloud dataset

Kaggle - Airbus Aircraft Detection Dataset

https://www.kaggle.com/airbusgeo/airbus-aircrafts-sample-dataset
One hundred civilian airports and over 3000 annotated commercial aircrafts
detecting-aircrafts-on-airbus-pleiades-imagery-with-yolov5
pytorch-remote-sensing -> Aircraft detection using the 'Airbus Aircraft Detection' dataset and Faster-RCNN with ResNet-50 backbone in pytorch

Kaggle - Airbus oil storage detection dataset

https://www.kaggle.com/airbusgeo/airbus-oil-storage-detection-dataset
Oil-Storage Tank Instance Segmentation with Mask R-CNN with accompanying article
Oil Storage Detection on Airbus Imagery with YOLOX -> uses the Kaggle Airbus Oil Storage Detection dataset

Kaggle - Satellite images of hurricane damage

Kaggle - Austin Zoning Satellite Images

https://www.kaggle.com/franchenstein/austin-zoning-satellite-images
classify a images of Austin into one of its zones, such as residential, industrial, etc. 3667 satellite images

Kaggle - Statoil/C-CORE Iceberg Classifier Challenge

Kaggle - Land Cover Classification Dataset from DeepGlobe Challenge - segmentation

https://www.kaggle.com/balraj98/deepglobe-land-cover-classification-dataset
Satellite Imagery Semantic Segmentation with CNN -> 7 different segmentation classes, DeepGlobe Land Cover Classification Challenge dataset, with repo
Land Cover Classification with U-Net -> Satellite Image Multi-Class Semantic Segmentation Task with PyTorch Implementation of U-Net, uses DeepGlobe Land Cover Segmentation dataset, with code
DeepGlobe Land Cover Classification Challenge solution

Kaggle - Next Day Wildfire Spread

A Data Set to Predict Wildfire Spreading from Remote-Sensing Data

Kaggle - Satellite Next Day Wildfire Spread

Inspired by the above dataset, using different data sources

Kaggle - xView2 challenge - assessing building damage after a natural disaster

The xView2 Challenge focuses on automating the process of assessing building damage after a natural disaster

https://www.kaggle.com/tunguz/xview2-challenge-dataset-train-and-test
https://xview2.org/
GitHub for the challenge
A look at IBM's approach -> used a single U-Net model
Simple Building detection example

Kaggle - Spacenet 7 Multi-Temporal Urban Change Detection

https://www.kaggle.com/datasets/amerii/spacenet-7-multitemporal-urban-development
SatFootprint -> building segmentation on the Spacenet 7 dataset

Kaggle - Satellite Images to predict poverty in Africa

https://www.kaggle.com/datasets/sandeshbhat/satellite-images-to-predict-povertyafrica
Uses satellite imagery and nightlights data to predict poverty levels at a local level
Predicting-Poverty -> Combining satellite imagery and machine learning to predict poverty, in PyTorch

Kaggle - NOAA Fisheries Steller Sea Lion Population Count

https://www.kaggle.com/competitions/noaa-fisheries-steller-sea-lion-population-count -> count sea lions from aerial images
Sealion-counting
Sealion_Detection_Classification

Kaggle - Arctic Sea Ice Image Masking

Kaggle - Overhead-MNIST

A Benchmark Satellite Dataset as Drop-In Replacement for MNIST
https://www.kaggle.com/datamunge/overheadmnist -> kaggle
https://arxiv.org/abs/2102.04266 -> paper
https://github.com/reveondivad/ov-mnist -> github

Kaggle - Satellite Image Classification

Kaggle - miscellaneous

https://www.kaggle.com/reubencpereira/spatial-data-repo -> Satellite + loan data
https://www.kaggle.com/towardsentropy/oil-storage-tanks -> Image data of industrial tanks with bounding box annotations, estimate tank fill % from shadows
https://www.kaggle.com/airbusgeo/airbus-wind-turbines-patches -> Airbus SPOT satellites images over wind turbines for classification
https://www.kaggle.com/aceofspades914/cgi-planes-in-satellite-imagery-w-bboxes -> CGI planes object detection dataset
https://www.kaggle.com/atilol/aerialimageryforroofsegmentation -> Aerial Imagery for Roof Segmentation
https://www.kaggle.com/andrewmvd/ship-detection -> 621 images of boats and ships
https://www.kaggle.com/alpereniek/vehicle-detection-from-satellite-images-data-set
https://www.kaggle.com/sergiishchus/maxar-satellite-data -> Example Maxar data at 15 cm resolution
https://www.kaggle.com/cici118/swimming-pool-detection-algarves-landscape
https://www.kaggle.com/datasets/donkroco/solar-panel-module -> object detection for solar panels
https://www.kaggle.com/datasets/balraj98/deepglobe-road-extraction-dataset -> segment roads

Synthetic data

Training data can be hard to acquire, particularly for rare events such as change detection after disasters, or imagery of rare classes of objects. In these situations, generating synthetic training data might be the only option. This has become quite sophisticated, with 3D models being use with open source games engines such as Unreal.

The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation with repo
RarePlanes -> incorporates both real and synthetically generated satellite imagery including aircraft. Read the arxiv paper and checkout this repo. Note the dataset is available through the AWS Open-Data Program for free download
Read this article from NVIDIA which discusses fine tuning a model pre-trained on synthetic data (Rareplanes) with 10% real data, then pruning the model to reduce its size, before quantizing the model to improve inference speed
Combining Synthetic Data with Real Data to Improve Detection Results in Satellite Imagery
BlenderGIS could be used for synthetic data generation
bifrost.ai -> simulated data service with geospatial output data formats
oktal-se -> software for generating simulated data across a wide range of bands including optical and SAR
The Nuances of Extracting Utility from Synthetic Data -> We find that strategically augmenting the real dataset is nearly as effective as adding synthetic data in the quest to improve the detection or rare object classes, and that fully extracting the utility of synthetic data is a nuanced process
Synthesizing Robustness -> explores how to best leverage and enhance synthetic data
rendered.ai -> The Platform as a Service for Creating Synthetic Data
synthetic_xview_airplanes -> creation of airplanes synthetic dataset using ArcGIS CityEngine
Combining Synthetic Data with Real Data to Improve Detection Results in Satellite Imagery: Case Study
SynImageAnalysis -> comparing syn and real sattlelite images in the latent feature space (embeddings)
Import OpenStreetMap data into Unreal Engine 4
deepfake-satellite-images -> dataset that includes over 1M images of synthetic aerial images
synthetic-disaster -> Generate synthetic satellite images of natural disasters using deep neural networks
STPLS3D -> A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
LESS -> LargE-Scale remote sensing data and image Simulation framework over heterogeneous 3D scenes
Synthesizing Robustness: Dataset Size Requirements and Geographic Insights -> Medium article, concludes that synthetic data is most beneficial to the rarest object classes and that extracting utility from synthetic data often takes significant effort and creativity
rs_img_synth -> code for 2020 paper: Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data

Online platforms for analytics

This article discusses some of the available platforms
Pangeo -> There is no single software package called “pangeo”; rather, the Pangeo project serves as a coordination point between scientists, software, and computing infrastructure. Includes open source resources for parallel processing using Dask and Xarray. Pangeo recently announced their 2.0 goals: pivoting away from directly operating cloud-based JupyterHubs, and towards eductaion and research
Descartes Labs -> access to EO imagery from a variety of providers via python API
Planet have a Jupyter notebook platform which can be deployed locally.
eurodatacube.com -> data & platform for EO analytics in Jupyter env, paid
up42 is a developer platform and marketplace, offering all the building blocks for powerful, scalable geospatial products
Microsoft Planetary Computer -> direct Google Earth Engine competitor in the making?
eofactory.ai -> supports multi public and private data sources that can be used to analyse and extract information
mapflow.ai -> imagery analysis platform with its instant access to the major satellite imagery providers, models for extract building footprints etc & QGIS plugin
openeo by ESA data platform
Adam platform -> the Advanced geospatial Data Management platform (ADAM) is a tool to access a large variety and volume of global environmental data

Free online compute

A GPU is required for training deep learning models (but not necessarily for inferencing), and this section lists a couple of free Jupyter environments with GPU available. There is a good overview of online Jupyter development environments on the fastai site. I personally use Colab Pro with data hosted on Google Drive, or Sagemaker if I have very long running training jobs.

Google Colab

Collaboratory notebooks with GPU as a backend for free for 12 hours at a time. Note that the GPU may be shared with other users, so if you aren't getting good performance try reloading.
Also a pro tier for $10 a month -> https://colab.research.google.com/signup
Tensorflow, pytorch & fastai available but you may need to update them
Colab Alive is a chrome extension that keeps Colab notebooks alive.
colab-ssh -> lets you ssh to a colab instance like it’s an EC2 machine and install packages that require full linux functionality

Kaggle - also Google!

Free to use
GPU Kernels - may run for 1 hour
Tensorflow, pytorch & fastai available but you may need to update them
Advantage that many datasets are already available

AWS SageMaker Studio Lab

SageMaker Studio Lab is a recent release which competes with Google colab being free to use with no credit card or AWS account required
Github profile

Others

Paperspace gradient -> free tier includes GPU usage
Deepnote -> many features for collaboration, GPU use is paid

State of the art engineering

Compute and data storage are on the cloud. Read how Planet and Airbus use the cloud
Traditional data formats aren't designed for processing on the cloud, so new standards are evolving such as COG and STAC
Google Earth Engine and Microsoft Planetary Computer are democratising access to 'planetary scale' compute
Google Colab and others are providing free acces to GPU compute to enable training deep learning models
No-code platforms and auto-ml are making ML techniques more accessible than ever
Serverless compute (e.g. AWS Lambda) mean that managing servers may become a thing of the past
Custom hardware is being developed for rapid training and inferencing with deep learning models, both in the datacenter and at the edge
Supervised ML methods typically require large annotated datasets, but approaches such as self-supervised and active learning require less or even no annotation
Computer vision traditionally delivered high performance image processing on a CPU by using compiled languages like C++, as used by OpenCV for example. The advent of GPUs are changing the paradigm, with alternatives optimised for GPU being created, such as Kornia
Whilst the combo of python and keras/tensorflow/pytorch are currently preeminent, new python libraries such as Jax and alternative languages such as Julia are showing serious promise

Cloud providers

An overview of the most relevant services provided by AWS, Google and Microsoft. Also consider one of the many smaller but more specialised platorms such as paperspace

AWS

Host your data on S3 and metadata in a db such as postgres
For batch processing use Batch. GPU instances are available for batch deep learning inferencing. See how Rastervision implement this here
If processing can be performed in 15 minutes or less, serverless Lambda functions are an attractive option owing to their ability to scale. Note that lambda may not be a particularly quick solution for deep learning applications, since you do not have the option to batch inference on a GPU. Creating a docker container with all the required dependencies can be a challenge. To get started read Using container images to run PyTorch models in AWS Lambda and for an image classification example checkout this repo. Also read Processing satellite imagery with serverless architecture which discusses queuing & lambda. Sagemaker also supports server less inference, see SageMaker Serverless Inference. For managing a serverless infrastructure composed of multiple lambda functions use AWS SAM and read How to continuously deploy a FastAPI to AWS Lambda with AWS SAM
Sagemaker is an ecosystem of ML tools accessed via a hosted Jupyter environment & API. Read Build GAN with PyTorch and Amazon SageMaker, Run computer vision inference on large videos with Amazon SageMaker asynchronous endpoints
SageMaker Studio Lab competes with Google colab being free to use with no credit card or AWS account required
Deep learning AMIs are EC2 instances with deep learning frameworks preinstalled. They do require more setup from the user than Sagemaker but in return allow access to the underlying hardware, which makes debugging issues more straightforward. There is a good guide to setting up your AMI instance on the Keras blog. Read Deploying the SpaceNet 6 Baseline on AWS
Specifically created for deep learning inferencing is AWS Inferentia
Rekognition custom labels is a 'no code' annotation, training and inferencing service. Read Training models using Satellite (Sentinel-2) imagery on Amazon Rekognition Custom Labels. For a comparison with Azure and Google alternatives read this article
Use Glue for data preprocessing - or use Sagemaker
To orchestrate basic data pipelines use Step functions. Use the AWS Step Functions Workflow Studio to get started. Read Orchestrating and Monitoring Complex, Long-running Workflows Using AWS Step Functions and checkout the aws-step-functions-data-science-sdk-python
If step functions are too limited or you want to write pipelines in python and use Directed Acyclic Graphs (DAGs) for workflow management, checkout hosted AWS managed Airflow. Read Orchestrate XGBoost ML Pipelines with Amazon Managed Workflows for Apache Airflow and checkout amazon-mwaa-examples
When developing you will definitely want to use boto3 and probably aws-data-wrangler
For managing infrastructure use Terraform. Alternatively if you wish to use TypeScript, JavaScript, Python, Java, or C# checkout AWS CDK, although I found relatively few examples to get going using python
AWS Ground Station now supports data delivery to Amazon S3
Redshift is a fast, scalable data warehouse that can extend queries to S3. Redshift is based on PostgreSQL but has some differences. Redshift supports geospatial data.
AWS App Runner enables quick deployment of containers as apps
AWS Athena allows running SQL queries against CSV files stored on S3. Serverless so pay only for the queries you run
If you are using pytorch checkout the S3 plugin for pytorch which provides streaming data access
Amazon AppStream 2.0 is a service to securely share desktop apps over the internet, see this post on sharing access to ArcGIS Pro
aws-gdal-robot -> A proof of concept implementation of running GDAL based jobs using AWS S3/Lambda/Batch
Building a robust data pipeline for processing Satellite Imagery at scale using AWS services & Airflow
Using artificial intelligence to detect product defects with AWS Step Functions -> demonstrates image classification workflow
sagemaker-defect-detection -> demonstrates object detection training and deployment
How do you process space data and imagery in low earth orbit? -> Snowcone is a standalone computer that can run AWS services at the edge, and has been demonstraed on the ISS (International space station)
Amazon OpenSearch -> can be used to create a visual search service
Automated Earth observation using AWS Ground Station Amazon S3 data delivery
Satellogic makes Earth observation data more accessible and affordable with AWS
Analyze terabyte-scale geospatial datasets with Dask and Jupyter on AWS
How SkyWatch built its satellite imagery solution using AWS Lambda and Amazon EFS
Identify mangrove forests using satellite image features using Amazon SageMaker Studio and Amazon SageMaker Autopilot
Detecting invasive Australian tree ferns in Hawaiian forests
Improve ML developer productivity with Weights & Biases: A computer vision example on Amazon SageMaker

Google Cloud

For storage use Cloud Storage (AWS S3 equivalent)
For data warehousing use BigQuery (AWS Redshift equivalent). Visualize massive spatial datasets directly in BigQuery using CARTO
For model training use Vertex (AWS Sagemaker equivalent)
For containerised apps use Cloud Run (AWS App Runner equivalent but can scale to zero)

Microsoft Azure

Azure Orbital -> Satellite ground station and scheduling services for fast downlinking of data
ShipDetection -> use the Azure Custom Vision service to train an object detection model that can detect and locate ships in a satellite image
SwimmingPoolDetection -> Swimming pool detection with Azure Custom Vision
Geospatial analysis with Azure Synapse Analytics and repo

Deploying models

This section discusses how to get a trained machine learning & specifically deep learning model into production. For an overview on serving deep learning models checkout Practical-Deep-Learning-on-the-Cloud. There are many options if you are happy to dedicate a server, although you may want a GPU for batch processing. For serverless use AWS lambda.

Rest API on dedicated server

A common approach to serving up deep learning model inference code is to wrap it in a rest API. The API can be implemented in python (flask or FastAPI), and hosted on a dedicated server e.g. EC2 instance. Note that making this a scalable solution will require significant experience.

Basic API: https://blog.keras.io/building-a-simple-keras-deep-learning-rest-api.html with code here
Advanced API with request queuing: https://www.pyimagesearch.com/2018/01/29/scalable-keras-deep-learning-rest-api/
How to make a geospatial Rest Api web service with Python, Flask and Shapely - Tutorial
BMW-YOLOv4-Training-Automation -> project that demos training ML model via rest API
Basic REST API for a keras model using FastAPI
NI4OS-RSSC -> Web Service for Remote Sensing Scene Classification (RS2C) using TensorFlow Serving and Flask
Sat2Graph Inference Server -> API in Go for road segmentation model inferencing
API algorithm to apply object detection model to terabyte size satellite images with 800% better performance and 8 times less resources usage
clearcut_detection -> django backend
airbus-ship-detection -> CNN with REST API

Framework specific model serving

If you are happy to live with some lock-in, these are good options:

Tensorflow serving is limited to Tensorflow models
Pytorch serve is easy to use, limited to Pytorch models, can be deployed via AWS Sagemaker
sagemaker-inference-toolkit -> Serve machine learning models within a Docker container using AWS SageMaker

Framework agnostic model serving

The Triton Inference Server provides an optimized cloud and edge inferencing solution. Read CAPE Analytics Uses Computer Vision to Put Geospatial Data and Risk Information in Hands of Property Insurance Companies
RedisAI is a Redis module for executing Deep Learning/Machine Learning models and managing their data

Using lambda functions - i.e. serverless

Using lambda functions allows inference without having to configure or manage the underlying infrastructure

On AWS either use regular lambdas from AWS or SageMaker Serverless Inference
Object detection inference with AWS Lambda and IceVision (PyTorch) with repo
Deploying PyTorch on AWS Lambda
Example deployment behind an API Gateway Proxy

Models in the browser

The model is run in the browser itself on live images, ensuring processing is always with the latest model available and removing the requirement for dedicated server side inferencing

Classifying satellite imagery - Made with TensorFlow.js YoutTube video

Model optimisation for deployment

The general approaches are outlined in this article from NVIDIA which discusses fine tuning a model pre-trained on synthetic data (Rareplanes) with 10% real data, then pruning the model to reduce its size, before quantizing the model to improve inference speed. There are also toolkits for optimisation, in particular ONNX which is framework agnostic.

Model monitoring

Once your model is deployed you will want to monitor for data errors, broken pipelines, and model performance degradation/drift ref

Blog post by Neptune: Doing ML Model Performance Monitoring The Right Way
whylogs -> Profile and monitor your ML data pipeline end-to-end

Image annotation

For supervised machine learning, you will require annotated images. For example if you are performing object detection you will need to annotate images with bounding boxes. Check that your annotation tool of choice supports large image (likely geotiff) files, as not all will. Note that GeoJSON is widely used by remote sensing researchers but this annotation format is not commonly supported in general computer vision frameworks, and in practice you may have to convert the annotation format to use the data with your chosen framework. There are both closed and open source tools for creating and converting annotation formats. Some of these tools are simply for performing annotation, whilst others add features such as dataset management and versioning. Note that self-supervised and active learning approaches might circumvent the need to perform a large scale annotation exercise. Note that tiffs/geotiffs cannot be displayed by most browsers (Chrome), but CAN render in Safari.

Annotation tools with GEO features

Also check the section Image handling, manipulation & dataset creation

GroundWork is designed for annotating and labeling geospatial data like satellite imagery, from Azavea
labelbox.com -> free tier is quite generous, supports annotating Geotiffs & returning annotations with geospatial coordinates. Watch this webcast
diffgram describes itself as a complete training data platform for machine learning delivered as a single application, supports streaming data to pytorch & tensorflow. COGS can be annotated
iris -> Tool for manual image segmentation and classification of satellite imagery
If you are considering building an in house annotation platform read this article. Used PostGis database, GeoJson format and GIS standard in a stateless architecture
satellite-imagery-labeling-tool -> from Microsoft, this is a lightweight web-interface for creating and sharing vector annotations over satellite/aerial imagery scenes
RSLabel -> remote sensing (RS) image annotation tool for deep learning
encord -> supports annotatin SAR

Open source annotation tools

awesome-data-labeling -> long list of annotation tools
awesome-open-data-annotation -> another long list of annotation tools
labelImg is the classic desktop tool, limited to bounding boxes for object detection. Also checkout roLabelImg which supports ROTATED rectangle regions, as often occurs in aerial imagery. labelImg_OBB is another fork supporting orinted bounding boxes (OBB)
Labelme is a very popular & simple dektop app for polygonal annotation suitable for object detection and semantic segmentation. Note it outputs annotations in a custom LabelMe JSON format which you will need to convert, e.g. using labelme2coco. Read Labelme Image Annotation for Geotiffs
Label Studio is a multi-type data labeling and annotation tool with standardized output format, syncing to buckets, and supports importing pre-annotations (create with a model). Checkout label-studio-converter for converting Label Studio annotations into common dataset formats
CVAT suports object detection, segmentation and classification via a local web app. There is an open issue to support large TIFF files. This article on Roboflow gives a good intro to CVAT. Checkout CVAT images validator
VoTT -> an electron app for building end to end Object Detection Models from Images and Videos, by Microsoft
Create your own annotation tool using Bokeh Holoviews, tkinter, or see these dash examples for object detection and segmentation
Deeplabel is a cross-platform tool for annotating images with labelled bounding boxes. Deeplabel also supports running inference using state-of-the-art object detection models like Faster-RCNN and YOLOv4. With support out-of-the-box for CUDA, you can quickly label an entire dataset using an existing model.
Alturos.ImageAnnotation is a collaborative tool for labeling image data on S3 for yolo
pigeonXT -> create custom image classification annotators within Jupyter notebooks
ipyannotations -> Image annotations in python using Jupyter notebooks
Label-Detect -> is a graphical image annotation tool and using this tool a user can also train and test large satellite images, fork of the popular labelImg tool
Swipe-Labeler -> Swipe Labeler is a Graphical User Interface based tool that allows rapid labeling of image data
SuperAnnotate can be run locally or used via a cloud service
dash_doodler -> A web application built with plotly/dash for image segmentation with minimal supervision
remo -> A webapp and Python library that lets you explore and control your image datasets
TensorFlow Object Detection API provides a handy utility for object annotation within Google Colab notebooks. See usage here
coco-annotator -> Web-based image segmentation tool for object detection, localization, and keypoints
pylabel -> Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo. PyLabel also includes an image labeling tool that runs in a Jupyter notebook that can annotate images manually or perform automatic labeling using a pre-trained model
BMW-Labeltool-Lite -> bounding box annotator
django-labeller -> An image labelling tool for creating segmentation data sets, for Django and Flask
scalabel -> supports 2D images and 3D point clouds
Detection-Label-Tool -> Change detection and object annotation, uses PyQt

Cloud hosted & paid annotation tools & services

Several open source tools are also available on the cloud, including CVAT, label-studio & Diffgram. In general cloud solutions will provide a lot of infrastructure and storage for you, as well as integration with outsourced annotators.

GroundWork is designed for annotating and labeling geospatial data like satellite imagery, from Azavea
labelbox.com -> free tier is quite generous, supports annotating Geotiffs & returning annotations with geospatial coordinates. Watch this webcast
Roboflow -> in addition to annotation this platform makes it easy to convert between annotation formats & manage datasets, as well as train and deploy custom models to private API endpoints. Read How to Train Computer Vision Models on Aerial Imagery
supervise.ly is one of the more fully featured platforms, decent free tier
AWS supports image annotation via the Rekognition Custom Labels console
rectlabel is a desktop app for MacOS to annotate images for bounding box object detection and segmentation, paid and free (rectlabel-lite) versions
hasty.ai -> supports model assisted annotation & inferencing

Annotation formats

Note there are many annotation formats, although PASCAL VOC and coco-json are the most commonly used. I recommend using geojson for storing polygons, then converting these to the required format when needed.

PASCAL VOC format: XML files in the format used by ImageNet
coco-json format: JSON in the format used by the 2015 COCO dataset
YOLO Darknet TXT format: contains one text file per image, used by YOLO
Tensorflow TFRecord: a proprietary binary file format used by the Tensorflow Object Detection API
Many more formats listed here
OBB: orinted bounding boxes are polygons representing rotated rectangles

Annotation visualisation & conversion tools

Tools to visualise annotations & convert between formats. Note that most annotation software will allow you to visualise existing annotations

Dataset-Converters -> a conversion toolset between different object detection and instance segmentation annotation formats
fiftyone -> open-source tool for building high quality datasets and computer vision models. Visualise labels, evaluate model predictions, explore scenarios of interest, identify failure modes, find annotation mistakes, and much more!
rebox -> Easily convert between bounding box annotation formats
Pascal VOC BBox Viewer
COCO-Assistant -> Helper for dealing with MS-COCO annotations; Merge datasets, Remove specfic category from dataset, Generate annotations statistics - distribution of object areas and category distribution
pybboxes -> Light weight toolkit for bounding boxes providing conversion between bounding box types and simple computations
voc2coco -> Convert VOC format XMLs to COCO format json
ObjectDetectionEval -> Parse all kinds of object detection databases (ImageNet, COCO, YOLO, PascalVOC, OpenImage, CVAT, LabelMe, etc.) & save to other formats
LabelMeYoloConverter -> Convert LabelMe Annotation Tool JSON format to YOLO text file format

Open source software

By software, I here mean desktop type apps. A note on licensing: The two general types of licenses for open source are copyleft and permissive. Copyleft requires that subsequent derived software products also carry the license forward, e.g. the GNU Public License (GNU GPLv3). For permissive, options to modify and use the code as one please are more open, e.g. MIT & Apache 2. Checkout choosealicense.com/

awesome-earthobservation-code -> lists many useful tools and resources
Orfeo toolbox - remote sensing toolbox with python API (just a wrapper to the C code). Do activites such as pansharpening, ortho-rectification, image registration, image segmentation & classification. Not much documentation.
QUICK TERRAIN READER - view DEMS, Windows
dl-satellite-docker -> docker files for geospatial analysis, including tensorflow, pytorch, gdal, xgboost...
AIDE V2 - Tools for detecting wildlife in aerial images using active learning
Land Cover Mapping web app from Microsoft
Solaris -> An open source ML pipeline for overhead imagery by CosmiQ Works, similar to Rastervision but with some unique very vool features
openSAR -> Synthetic Aperture Radar (SAR) Tools and Documents from Earth Big Data LLC
YMIR -> YMIR provides a Rapid Data-centric Development Platform for Vision Applications. Read the paper here.
qhub -> QHub enables teams to build and maintain a cost effective and scalable compute/data science platform in the cloud.
imagej -> a very versatile image viewer and processing program
Geo Data Viewer extension for VSCode which enables opening and viewing various geo data formats with nice visualisations
Datasette is a tool for exploring and publishing data as an interactive website and accompanying API, with SQLite backend. Various plugins extend its functionality, for example to allow displaying geospatial info, render images (useful for thumbnails), and add user authentication. Available as a desktop app. Read Drawing shapes on a map to query a SpatiaLite database
Photoprism is a privately hosted app for browsing, organizing, and sharing your photo collection, with support for tiffs
dbeaver is a free universal database tool and SQL client with geospatial features
Grafana can be used to make interactive dashboards, checkout this example showing Point data. Note there is an AWS managed service for Grafana
litestream -> Continuously stream SQLite changes to S3-compatible storage
ImageFusion) -> Temporal fusion of raster image time-Series
nvtop -> NVIDIA GPUs htop like monitoring tool
rgis -> Geospatial data viewer written in Rust
aerialbot -> A simple yet highly configurable bot that tweets geotagged aerial imagery of a random location in the world

General utilities

Scripts and command line applications

geospatial-cli -> a collection of geospatial programs with commandline interface
PyShp -> The Python Shapefile Library (PyShp) reads and writes Shapefiles in pure Python
s2p -> a Python library and command line tool that implements a stereo pipeline which produces elevation models from images taken by high resolution optical satellites such as Pléiades, WorldView, QuickBird, Spot or Ikonos
EarthPy -> A set of helper functions to make working with spatial data in open source tools easier. readExploratory Data Analysis (EDA) on Satellite Imagery Using EarthPy
pygeometa -> provides a lightweight and Pythonic approach for users to easily create geospatial metadata in standards-based formats using simple configuration files
pesto -> PESTO is designed to ease the process of packaging a Python algorithm as a processing web service into a docker image. It contains shell tools to generate all the boiler plate to build an OpenAPI processing web service compliant with the Geoprocessing-API. By Airbus Defence And Space
GEOS -> Google Earth Overlay Server (GEOS) is a python-based server for creating Google Earth overlays of tiled maps. Your can also display maps in the web browser, measure distances and print maps as high-quality PDF’s.
GeoDjango intends to be a world-class geographic Web framework. Its goal is to make it as easy as possible to build GIS Web applications and harness the power of spatially enabled data. Some features of GDAL are supported.
rasterstats -> summarize geospatial raster datasets based on vector geometries
turfpy -> a Python library for performing geospatial data analysis which reimplements turf.js
rsgislib -> Remote Sensing and GIS Software Library; python module tools for processing spatial and image data
eo-learn -> seamlessly access and process spatio-temporal image sequences acquired by any satellite fleet in a timely and automatic manner. See eo-learn-examples
RStoolbox: Tools for Remote Sensing Data Analysis in R
nd -> Framework for the analysis of n-dimensional, multivariate Earth Observation data, built on xarray
reverse-geocoder -> a fast, offline reverse geocoder in Python
MuseoToolBox -> a python library to simplify the use of raster/vector, especially for machine learning and remote sensing
py6s -> an interface to the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) atmospheric Radiative Transfer Model
timvt -> PostGIS based Vector Tile server built on top of the modern and fast FastAPI framework
titiler -> A dynamic Web Map tile server using FastAPI
BRAILS -> an AI-based pipeline for city-scale building information modelling (BIM)
color-thief-py -> Grabs the dominant color or a representative color palette from an image
force -> an all-in-one processing engine for medium-resolution Earth Observation image archives
mapwarper -> an open source map geo-rectification, warping and georeferencing application
sarpy -> A basic Python library to demonstrate reading, writing, display, and simple processing of complex SAR data using the NGA SICD standard
buzzard -> Advanced raster and geometry manipulations
sentinel1denoised -> Thermal noise subtraction, scalloping correction, angular correction
RStoolbox -> Remote Sensing Data Analysis in R
kart -> Distributed version-control for geospatial and tabular data
picogeojson -> a Python library for reading, writing, and working with GeoJSON
shareloc -> a simple remote sensing geometric library, to perform image coordinates projections between sensor and ground and vice versa
geoblaze -> Blazing Fast JavaScript Raster Processing Engine
nasa-wildfires -> Download wildfire hotspots detected by NASA satellites and the Fire Information for Resource Management System (FIRMS)
SSGP-toolbox -> Simple Spatial Gapfilling Processor. Toolbox for filling gaps in spatial datasets
imgreg2D -> 2D image registration in python, using napari
georust -> A collection of geospatial tools and libraries written in Rust
DataPillager -> Download data from Esri REST service
litexplore -> a Python web app that lets you explore remote SQLite databases over SSH connections
tifeatures -> Simple and Fast Geospatial Features API for PostGIS
pyroSAR -> framework for large-scale SAR satellite data processing
S1_NRB -> A prototype processor for the Sentinel-1 Normalised Radar Backscatter product
AGBench -> a Python library that benchmarks satellite-based aboveground biomass or carbon estimate maps
mbtiles-s3-server -> Python server to on-the-fly extract and serve vector tiles from an mbtiles file on S3
matico -> a set of tools and services that allow users to manage geospatial datasets, build APIs that use those datasets and full geospatial applications with little to no code

Low level numerical & data formats

xarray -> N-D labeled arrays and datasets. Read Handling multi-temporal satellite images with Xarray. Checkout xarray_leaflet for tiled map plotting and sklearn-xarray for metadata-aware machine learning. Publish Xarray Datasets via a REST API uisng xpublish
wxee -> Export data from GEE to xarray using wxee then train with pytorch or tensorflow models. Useful since GEE only suports tfrecord export natively
xarray-spatial -> Fast, Accurate Python library for Raster Operations. Implements algorithms using Numba and Dask, free of GDAL
xarray-beam -> Distributed Xarray with Apache Beam by Google
Geowombat -> geo-utilities applied to air and space-borne imagery, uses Rasterio, Xarray and Dask for I/O and distributed computing with named coordinates. Create Land Use Classification using Geowombat & Sklearn
NumpyTiles -> a specification for providing multiband full-bit depth raster data in the browser
Zarr -> Zarr is a format for the storage of chunked, compressed, N-dimensional arrays. Zarr depends on NumPy
geoparquet -> Specification for storing geospatial vector data (point, line, polygon) in Parquet
TFRecord reader for PyTorch

Image processing, handling, manipulation

Pillow is the Python Imaging Library -> this will be your go-to package for image manipulation in python
opencv-python is pre-built CPU-only OpenCV packages for Python
kornia is a differentiable computer vision library for PyTorch, like openCV but on the GPU. Perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors.
tifffile -> Read and write TIFF files
xtiff -> A small Python 3 library for writing multi-channel TIFF stacks
geotiff -> A noGDAL tool for reading and writing geotiff files
geolabel-maker -> combine satellite or aerial imagery with vector spatial data to create your own ground-truth dataset in the COCO format for deep-learning models
imagehash -> Image hashes tell whether two images look nearly identical
fake-geo-images -> A module to programmatically create geotiff images which can be used for unit tests
imagededup -> Finding duplicate images made easy! Uses perceptual hashing
duplicate-img-detection -> A basic duplicate image detection service using perceptual image hash functions and nearest neighbor search, implemented using faiss, fastapi, and imagehash
rmstripes -> Remove stripes from images with a combined wavelet/FFT approach
activeloopai Hub -> The fastest way to store, access & manage datasets with version-control for PyTorch/TensorFlow. Works locally or on any cloud. Scalable data pipelines.
sewar -> All image quality metrics you need in one package
Satellite imagery label tool -> provides an easy way to collect a random sample of labels over a given scene of satellite imagery
Missing-Pixel-Filler -> given images that may contain missing data regions (like satellite imagery with swath gaps), returns these images with the regions filled
color_range_filter -> a script that allows us to find range of colors in images using openCV, and then convert them into geo vectors
eo4ai -> easy-to-use tools for preprocessing datasets for image segmentation tasks in Earth Observation
rasterix -> a cross-platform utility built around the GDAL library and the Qt framework designed to process geospatial raster data
datumaro -> Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets
sentinelPot -> a python package to preprocess Sentinel 1&2 imagery
ImageAnalysis -> Aerial imagery analysis, processing, and presentation scripts.
rastertodataframe -> Convert any GDAL compatible raster to a Pandas DataFrame
yeoda -> provides lower and higher-level data cube classes to work with well-defined and structured earth observation data
tiles-to-tiff -> Python script for converting XYZ raster tiles for slippy maps to a georeferenced TIFF image
telluric -> a Python library to manage vector and raster geospatial data in an interactive and easy way
Sniffer -> A python application for sorting through geospatial imagery
pyjeo -> a library for image processing for geospatial data implemented in JRC Ispra, with paper
vpv -> Image viewer designed for image processing experts
arop -> Automated Registration and Orthorectification Package
satellite_image -> Python package to process images from Landsat satellites and return geographic information, cloud mask, numpy array, geotiff
large_image -> Python modules to work with large multiresolution images
ResizeRight -> The correct way to resize images or tensors. For Numpy or Pytorch (differentiable)
pysat -> a package providing a simple and flexible interface for downloading, loading, cleaning, managing, processing, and analyzing scientific measurements
plcompositor -> c++ tool from Planet to create seamless and cloudless image mosaics from deep stacks of satellite imagery

Image chipping/tiling & merging

Since raw images can be very large, it is usually necessary to chip/tile them into smaller images before annotation & training

image_slicer -> Split images into tiles. Join the tiles back together
tiler by nuno-faria -> split images into tiles and merge tiles into a large image
tiler by the-lay -> N-dimensional NumPy array tiling and merging with overlapping, padding and tapering
xbatcher -> Xbatcher is a small library for iterating xarray DataArrays in batches. The goal is to make it easy to feed xarray datasets to machine learning libraries such as Keras
GeoTagged_ImageChip -> A simple script to create geo tagged image chips from high resolution RS iamges for training deep learning models such as Unet
geotiff-crop-dataset -> A Pytorch Dataloader for tif image files that dynamically crops the image
Train-Test-Validation-Dataset-Generation -> app to crop images and create small patches of a large image e.g. Satellite/Aerial Images, which will then be used for training and testing Deep Learning models specifically semantic segmentation models
satproc -> Python library and CLI tools for processing geospatial imagery for ML
Sliding Window -> break large images into a series of smaller chunks
patchify -> A library that helps you split image into small, overlappable patches, and merge patches into original image
split-rs-data -> Divide remote sensing images and their labels into data sets of specified size
image-reconstructor-patches -> Reconstruct Image from Patches with a Variable Stride
rpc_cropper -> A small standalone tool to crop satellite images and their RPC
geotile -> python library for tiling the geographic raster data
GeoPatch -> generating patches from remote sensing data

Image dataset creation

Many datasets on kaggle & elsewhere have been created by screen-clipping Google Maps or browsing web portals. The tools below are to create datasets programatically

MapTilesDownloader -> A super easy to use map tiles downloader built using Python
jimutmap -> get enormous amount of high resolution satellite images from apple / google maps quickly through multi-threading
google-maps-downloader -> A short python script that downloads satellite imagery from Google Maps
ExtractSatelliteImagesFromCSV -> extract satellite images using a CSV file that contains latitude and longitude, uses mapbox
sentinelsat -> Search and download Copernicus Sentinel satellite images
SentinelDownloader -> a high level wrapper to the SentinelSat that provides an object oriented interface, asynchronous downloading, quickview & simpler searching methods
GEES2Downloader -> Downloader for GEE S2 bands
Sentinel-2 satellite tiles images downloader from Copernicus -> Minimizes data download and combines multiple tiles to return a single area of interest
felicette -> Satellite imagery for dummies. Generate JPEG earth imagery from coordinates/location name with publicly available satellite data
Easy Landsat Download
A simple python scrapper to get satellite images of Africa, Europe and Oceania's weather using the Sat24 website
RGISTools -> Tools for Downloading, Customizing, and Processing Time Series of Satellite Images from Landsat, MODIS, and Sentinel
DeepSatData -> Automatically create machine learning datasets from satellite images
landsat_ingestor -> Scripts and other artifacts for landsat data ingestion into Amazon public hosting
satpy -> a python library for reading and manipulating meteorological remote sensing data and writing it to various image and data file formats
GIBS-Downloader -> a command-line tool which facilitates the downloading of NASA satellite imagery and offers different functionalities in order to prepare the images for training in a machine learning pipeline
eodag -> Earth Observation Data Access Gateway
pylandsat -> Search, download, and preprocess Landsat imagery
landsatxplore -> Search and download Landsat scenes from EarthExplorer
OpenSarToolkit -> High-level functionality for the inventory, download and pre-processing of Sentinel-1 data in the python language
lsru -> Query and Order Landsat Surface Reflectance data via ESPA
eoreader -> Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and index in a sensor-agnostic way
Export thumbnails from Earth Engine
deepsentinel-osm -> A repository to generate land cover labels from OpenStreetMap
img2dataset -> Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine
ohsome2label -> Historical OpenStreetMap (OSM) Objects to Machine Learning Training Samples
Label Maker -> downloads OpenStreetMap QA Tile information and satellite imagery tiles and saves them as an .npz file for use in machine learning training. This should be used instead of the deprecated skynet-data
sentinel2tools -> downloading & basic processing of Sentinel 2 imagesry. Read Sentinel2tools: simple lib for downloading Sentinel-2 satellite images
Aerial-Satellite-Imagery-Retrieval -> A program using Bing maps tile system to automatically download Aerial / Satellite Imagery given a lat/lon bounding box and level of detail
google-maps-at-88-mph -> Google Maps keeps old satellite imagery around for a while – this tool collects what's available for a user-specified region in the form of a GIF

Image augmentation packages

Image augmentation is a technique used to expand a training dataset in order to improve ability of the model to generalise

AugLy -> A data augmentations library for audio, image, text, and video. By Facebook
albumentations -> Fast image augmentation library and an easy-to-use wrapper around other libraries
FoHIS -> Towards Simulating Foggy and Hazy Images and Evaluating their Authenticity
Kornia provides augmentation on the GPU
toolbox by ming71 -> various cv tools, such as label tools, data augmentation, label conversion, etc.
AstroAugmentations -> augmentations designed around astronomical instruments
Chessmix -> data augmentation method for remote sensing semantic segmentation
satellite_object_augmentation -> Object-based augmentation for remote sensing images segmentation via CNN
hypernet -> hyperspectral data augmentation

Image formats, data management and catalogues

GeoServer -> an open source server for sharing geospatial data
Open Data Cube - serve up cubes of data https://www.opendatacube.org/
https://terria.io/ for pretty catalogues
Large datasets may come in HDF5 format, can view with -> https://www.hdfgroup.org/downloads/hdfview/
Climate data is often in netcdf format, which can be opened using xarray
The xarray docs list a number of ways that data can be stored and loaded.
TileDB -> a 'Universal Data Engine' to store, analyze and share any data (beyond tables), with any API or tool (beyond SQL) at planet-scale (beyond clusters), open source and managed options. Recently hiring to work with xarray, dask, netCDF and cloud native storage
BigVector database -> A fully-managed, highly-scalable, and cost-effective database for vectors. Vectorize structured data or orbital imagery and discover new insights
Read about Serverless PostGIS on AWS Aurora
Hub -> The fastest way to store, access & manage datasets with version-control for PyTorch/TensorFlow. Works locally or on any cloud. Read Faster Machine Learning Using Hub by Activeloop: A code walkthrough of using the hub package for satellite imagery
A Comparison of Spatial Functions: PostGIS, Athena, PrestoDB, BigQuery vs RedShift
Unfolded Studio -> visualization platform building on open source geospatial technologies including kepler.gl, deck.gl and H3. Processing is performed browser side enabling very responsive visualisations.
DroneDB -> can index and extract useful information from the EXIF/XMP tags of aerial images to display things like image footprint, flight path and image GPS location
embeddinghub -> A vector database for machine learning embeddings
Resonant GeoData -> a Django application well suited for catalogging and searching annotated geospatial imagery, shapefiles, and full motion video datasets
fastdup -> a tool for gaining insights from a large image collection. It can find anomalies, duplicate and near duplicate images

Deep learning packages, frameworks & projects

TorchGeo -> a PyTorch domain library providing datasets, samplers, transforms, and pre-trained models specific to geospatial data, supported by Microsoft. Read Geospatial deep learning with TorchGeo
rastervision -> An open source Python framework for building computer vision models on aerial, satellite, and other large imagery sets
torchrs -> PyTorch implementation of popular datasets and models in remote sensing tasksenhance) -> Enhance PyTorch vision for semantic segmentation, multi-channel images and TIF file
DeepHyperX -> A Python/pytorch tool to perform deep learning experiments on various hyperspectral datasets
DELTA -> Deep Earth Learning, Tools, and Analysis, by NASA is a framework for deep learning on satellite imagery, based on Tensorflow & using MLflow for tracking experiments
Lightly is a computer vision framework for training deep learning models using self-supervised learning
Icevision offers a curated collection of hundreds of high-quality pre-trained models within an easy to use framework
pytorch_eo -> aims to make Deep Learning for Earth Observation data easy and accessible to real-world cases and research alike
NGVEO -> applying convolutional neural networks (CNN) to Earth Observation (EO) data from Sentinel 1 and 2 using python and PyTorch
chip-n-scale-queue-arranger by developmentseed -> an orchestration pipeline for running machine learning inference at scale. Supports fastai models
http://spaceml.org/ -> A Machine Learning toolbox and developer community building the next generation AI applications for space science and exploration
TorchSat is an open-source deep learning framework for satellite imagery analysis based on PyTorch (no activity since June 2020)
DeepNetsForEO -> Uses SegNET for working on remote sensing images using deep learning (no activity since 2019)
RoboSat -> semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds (no longer maintained)
DeepOSM -> Train a deep learning net with OpenStreetMap features and satellite imagery (no activity since 2017)
mapwith.ai -> AI assisted mapping of roads with OpenStreetMap. Part of Open-Mapping-At-Facebook
sahi -> A vision library for performing sliced inference on large images/small objects. Read the arxiv paper and article A practical guide to using Slicing-Aided Hyper Inference for analyzing satellite images
terragpu -> Python library to process and classify remote sensing imagery by means of GPUs and AI/ML
EOTorchLoader -> Pytorch dataloader and pytorch lightning datamodule for Earth Observation imagery
satellighte -> an image classification library that consist state-of-the-art deep learning methods, using PyTorch Lightning
aeronetlib -> Python library to work with geospatial raster and vector data for deep learning
rsi-semantic-segmentation -> A unified PyTorch framework for semantic segmentation from remote sensing imagery
AiTLAS -> implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images
mmsegmentation -> Semantic Segmentation Toolbox with support for many remote sensing datasets including LoveDA , Potsdam, Vaihingen & iSAID
ODEON landcover -> a set of command-line tools performing semantic segmentation on remote sensing images (aerial and/or satellite) with as many layers as you wish

Model tracking, versioning, specification & compilation

dvc -> a git extension to keep track of changes in data, source code, and ML models together
Weights and Biases -> keep track of your ML projects. Log hyperparameters and output metrics from your runs, then visualize and compare results and quickly share findings with your colleagues
geo-ml-model-catalog -> provides a common metadata definition for ML models that operate on geospatial data
hummingbird -> a library for compiling trained traditional ML models into tensor computations, e.g. scikit learn model to pytorch for fast inference on a GPU
deepchecks -> Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort
pachyderm -> Data Versioning and Pipelines for MLOps. Read Pachyderm + Label Studio which discusses versioning and lineage of data annotations

Graphing and visualisation

hvplot -> A high-level plotting API for the PyData ecosystem built on HoloViews. Allows overlaying data on map tiles, see Exploring USGS Terrain Data in COG format using hvPlot
Pyviz examples include several interesting geospatial visualisations
napari -> napari is a fast, interactive, multi-dimensional image viewer for Python. It’s designed for browsing, annotating, and analyzing large multi-dimensional images. By integrating closely with the Python ecosystem, napari can be easily coupled to leading machine learning and image analysis tools. Note that to view a 3GB COG I had to install the napari-tifffile-reader plugin.
pixel-adjust -> Interactively select and adjust specific pixels or regions within a single-band raster. Built with rasterio, matplotlib, and panel.
Plotly Dash can be used for making interactive dashboards
folium -> a python wrapper to the excellent leaflet.js which makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map. Also checkout the streamlit-folium component for adding folium maps to your streamlit apps
ipyearth -> An IPython Widget for Earth Maps
geopandas-view -> Interactive exploration of GeoPandas GeoDataFrames
geogif -> Turn xarray timestacks into GIFs
leafmap -> geospatial analysis and interactive mapping with minimal coding in a Jupyter environment
xmovie -> A simple way of creating movies from xarray objects
acquisition-time -> Drawing (Satellite) acquisition dates in a timeline
splot -> Lightweight plotting for geospatial analysis in PySAL
prettymaps -> A small set of Python functions to draw pretty maps from OpenStreetMap data
Tools to Design or Visualize Architecture of Neural Network
AstronomicAL -> An interactive dashboard for visualisation, integration and classification of data using Active Learning
pyodi -> A simple tool for explore your object detection dataset
Interactive-TSNE -> a tool that provides a way to visually view a PyTorch model's feature representation for better embedding space interpretability
fastgradio -> Build fast gradio demos of fastai learners
pysheds -> Simple and fast watershed delineation in python
mapboxgl-jupyter -> Use Mapbox GL JS to visualize data in a Python Jupyter notebook
cartoframes -> integrate CARTO maps, analysis, and data services into data science workflows
datashader -> create meaningful representations of large datasets quickly and flexibly. Read Creating Visual Narratives from Geospatial Data Using Open-Source Technology Maxar blog post
Kaleido -> Fast static image export for web-based visualization libraries with zero dependencies
Embedding Projector in Wandb -> allows users to plot multi-dimensional embeddings on a 2D plane using common dimension reduction algorithms like PCA, UMAP, and t-SNE
PlotNeuralNet -> Latex code for making neural networks diagrams
Damage Assessment Visualizer -> leverages satellite imagery from a disaster region to visualize conditions of building and structures before and after a disaster
NN-SVG -> is a tool for creating Neural Network (NN) architecture drawings parametrically rather than manually
bbox-visualizer -> Make drawing and labeling bounding boxes easy as cake
jupyter-bbox-widget -> A Jupyter widget for annotating images with bounding boxes
EOmaps -> A library to create interactive maps of geographical datasets
H3-Pandas -> Integrates H3 with GeoPandas and Pandas
gmplot -> a matplotlib-like interface to render all the data you'd like on top of Google Maps
NPYViewer -> a simple GUI tool that provides multiple ways to view .npy files containing 2D NumPy Arrays
pyGEOVis -> Visualize geo-tiff/json based on folium
bokeh-tiler -> Tile large geospatial images for use in Bokeh. Read Serving up SpaceNet Imagery for Bokeh
torchshow -> Visualize PyTorch tensor in one-line of code
pixels -> Mapping and charting pixels from remote sensing Earth observation data with JavaScript
MulimgViewer -> a multi-image viewer that can open multiple images in one interface
cnn-explainer -> Learning Convolutional Neural Networks with Interactive Visualization
Overlay-GeoTiff-Raster-with-nodata-On-Interactive-Map
shapefile2gif -> Given a shapefile with time-annotated vector objects (e.g., building footprints + construction year), this script will automatically create an animated GIF illustrating the dynamics for a user-specified period of time

Algorithms

WaterDetect -> an end-to-end algorithm to generate open water cover mask, specially conceived for L2A Sentinel 2 imagery. It can also be used for Landsat 8 images and for other multispectral clustering/segmentation tasks.
GatorSense Hyperspectral Image Analysis Toolkit -> This repo contains algorithms for Anomaly Detectors, Classifiers, Dimensionality Reduction, Endmember Extraction, Signature Detectors, Spectral Indices
detectree -> Tree detection from aerial imagery
pylandstats -> compute landscape metrics
dg-calibration -> Coefficients and functions for calibrating DigitalGlobe imagery
python-fmask -> Implementation in Python of the cloud and shadow algorithms known collectively as Fmask
pyshepseg -> Python implementation of image segmentation algorithm of Shepherd et al (2019) Operational Large-Scale Segmentation of Imagery Based on Iterative Elimination.
Shadow-Detection-Algorithm-for-Aerial-and-Satellite-Images -> shadow detection and correction algorithm
faiss -> A library for efficient similarity search and clustering of dense vectors, e.g. image embeddings
awesome-spectral-indices -> A ready-to-use curated list of Spectral Indices for Remote Sensing applications
urban-footprinter -> A convolution-based approach to detect urban extents from raster datasets
ocean_color -> Tools and algorithms for drone and satellite based ocean color science
poliastro -> pure Python library for interactive Astrodynamics and Orbital Mechanics, with a focus on ease of use, speed, and quick visualization
acolite -> generic atmospheric correction module
pmapper -> a super-resolution and deconvolution toolkit for python. PMAP stands for Poisson Maximum A-Posteriori, a highly flexible and adaptable algorithm for these problems
pylandtemp -> Algorithms for computing global land surface temperature and emissivity from NASA's Landsat satellite images with Python
sarsen -> Algorithms and utilities for Synthetic Aperture Radar (SAR) sensors
sun-position -> code for computing sun position
simple_ortho -> Fast and simple orthorectification of images with known DEM and camera model
imageResolution -> Simple spatial resolution calculator for nadir & oblique aerial imagery
Spectral-Clustering -> normalized and unnormalized spectral clustering algorithms
Fogpy -> nowcasting of fog and low stratus clouds
orthorectification -> Orthorectification in Python. Note that all of this functionality already exists in libraries like GDAL and others. The goal of this codebase was to present and deep dive into these subroutines
Flood-Severity-Estimation -> estimate the height of the water in geo-referenced photos that depict floods using DEMs from JAXA
coastline-extraction -> Methods to identify and extract coastline from remote sensed data
Near real-time shadow detection and removal in remote sensing imagery application
image-registration -> using Point Feature Detection, Normalized DLT, RANSAC & Image Warping
pyTSEB -> A python Two Source Energy Balance model for estimation of evapotranspiration with remote sensing data
libpredict -> satellite orbit prediction library
GOTCHA -> Command line implementation of the GOTCHA stereo matching algorithm
SREM -> A Simplified and Robust Surface Reflectance Estimation Method for Satellite Imagery
kaizen -> A library to map match and help tackle the problem of overlapping/intersecting road and building footprint that arises in the process of map making

GDAL & Rasterio

So improtant this pair gets their own section. GDAL is THE command line tool for reading and writing raster and vector geospatial data formats. If you are using python you will probably want to use Rasterio which provides a pythonic wrapper for GDAL

GDAL and on twitter
GDAL is a dependency of Rasterio and can be difficult to build and install. I recommend using conda, brew (on OSX) or docker in these situations
GDAL docker quickstart: docker pull osgeo/gdal then docker run --rm -v $(pwd):/data/ osgeo/gdal gdalinfo /data/cog.tiff
Even Rouault maintains GDAL, please consider sponsoring him
Rasterio -> reads and writes GeoTIFF and other raster formats and provides a Python API based on Numpy N-dimensional arrays and GeoJSON. There are a variety of plugins that extend Rasterio functionality.
rio-cogeo -> Cloud Optimized GeoTIFF (COG) creation and validation plugin for Rasterio.
rioxarray -> geospatial xarray extension powered by rasterio
aws-lambda-docker-rasterio -> AWS Lambda Container Image with Python Rasterio for querying Cloud Optimised GeoTiffs. See this presentation
godal -> golang wrapper for GDAL
Write rasterio to xarray
Loam: A Client-Side GDAL Wrapper for Javascript
Short list of useful GDAL commands while working in data science for remote sensing
gdal-segment -> implements various segmentation algorithms over raster images
aws-gdal-robot -> A proof of concept implementation of running GDAL based jobs using AWS S3/Lambda/Batch
gdal2tiles -> A python library for generating map tiles based on gdal2tiles.py from GDAL project
gdal3.js -> Convert raster and vector geospatial data to various formats and coordinate systems entirely in the browser

Cloud Optimised GeoTiff (COG)

A Cloud Optimized GeoTIFF (COG) is a regular GeoTIFF that supports HTTP range requests, enabling downloading of specific tiles rather than the full file. COG generally work normally in GIS software such as QGIS, but are larger than regular GeoTIFFs

https://www.cogeo.org/
cog-best-practices
COGs in production
rio-cogeo -> Cloud Optimized GeoTIFF (COG) creation and validation plugin for Rasterio.
aiocogeo -> Asynchronous cogeotiff reader (python asyncio)
Landsat data in cloud optimised (COG) format analysed for NVDI with medium article Cloud Native Geoprocessing of Earth Observation Satellite Data with Pangeo.
Working with COGS and STAC in python using geemap
Load, Experiment, and Download Cloud Optimized Geotiffs (COG) using Python with Google Colab -> short read which covers finding COGS, opening with Rasterio and doing some basic manipulations, all in a Colab Notebook.
Exploring USGS Terrain Data in COG format using hvPlot -> local COG from public AWS bucket, open with rioxarray, visualise with hvplot. See the Jupyter notebook
aws-lambda-docker-rasterio -> AWS Lambda Container Image with Python Rasterio for querying Cloud Optimised GeoTiffs. See this presentation
cogbeam -> a python based Apache Beam pipeline, optimized for Google Cloud Dataflow, which aims to expedite the conversion of traditional GeoTIFFs into COGs
cogserver -> Expose a GDAL file as a HTTP accessible on-the-fly COG
Displaying a gridded dataset on a web-based map - Step by step guide for displaying large GeoTIFFs, using Holoviews, Bokeh, and Datashader
cog_worker -> Scalable arbitrary analysis on COGs

SpatioTemporal Asset Catalog specification (STAC)

The STAC specification provides a common metadata specification, API, and catalog format to describe geospatial assets, so they can more easily indexed and discovered.

Spec at https://github.com/radiantearth/stac-spec
STAC 1.0.0: The State of the STAC Software Ecosystem
Planet Disaster Data catalogue has the catalogue source on Github and uses the stac-browser
Getting Started with STAC APIs intro article
SpatioTemporal Asset Catalog API specification -> an API to make geospatial assets openly searchable and crawlable
stacindex -> STAC Catalogs, Collections, APIs, Software and Tools
Several useful repos on https://github.com/sat-utils
Intake-STAC -> Intake-STAC provides an opinionated way for users to load Assets from STAC catalogs into the scientific Python ecosystem. It uses the intake-xarray plugin and supports several file formats including GeoTIFF, netCDF, GRIB, and OpenDAP.
sat-utils/sat-search -> Sat-search is a Python 3 library and a command line tool for discovering and downloading publicly available satellite imagery using STAC compliant API
franklin -> A STAC/OGC API Features Web Service focused on ease-of-use for end-users.
stacframes -> A Python library for working with STAC Catalogs via Pandas DataFrames
sat-api-pg -> A Postgres backed STAC API
stactools -> Command line utility and Python library for STAC
pystac -> Python library for working with any STAC Catalog
STAC Examples for Nightlights data -> minimal example STAC implementation for the Light Every Night dataset of all VIIRS DNB and DMSP-OLS nighttime satellite data
stackstac -> Turn a STAC catalog into a dask-based xarray
stac-fastapi -> STAC API implementation with FastAPI
stac-fastapi-elasticsearch -> Elasticsearch backend for stac-fastapi
ml-aoi -> An Item and Collection extension to provide labeled training data for machine learning models
Discoverable and Reusable ML Workflows for Earth Observation -> part 1 and part 2 with the Geospatial Machine Learning Model Catalog (GMLMC)
eoAPI -> Earth Observation API with STAC + dynamic Raster/Vector Tiler
stac-nb -> STAC in Jupyter Notebooks
xstac -> Generate STAC Collections from xarray datasets
qgis-stac-plugin -> QGIS plugin for reading STAC APIs
cirrus-geo -> a STAC-based processing pipeline
stac-interactive-search -> A simple (browser based) UI for searching STAC APIs
easystac -> A Python package for simple STAC queries
stacmap -> Explore STAC items with an interactive map
odc-stac -> Load STAC items into xarray Datasets. Process locally or distribute data loading and computation with Dask.
AWS Lambda SenCloud Monitoring -> keep up-to-date your own derived data from the Sentinel-2 COG imagery archive using AWS lambda
stac-geoparquet -> Convert STAC items to geoparquet

OpenStreetMap

OpenStreetMap (OSM) is a map of the world, created by people like you and free to use under an open license. Quite a few publications use OSM data for annotations & ground truth. Note that the data is created by volunteers and the quality can be variable

osmnx -> Retrieve, model, analyze, and visualize data from OpenStreetMap
ohsome2label -> Historical OpenStreetMap Objects to Machine Learning Training Samples
Label Maker -> downloads OpenStreetMap QA Tile information and satellite imagery tiles and saves them as an .npz file for use in machine learning training. This should be used instead of the deprecated skynet-data
prettymaps -> A small set of Python functions to draw pretty maps from OpenStreetMap data
Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps -> fusion based architectures and coarse-to-fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks, arxiv paper
Identifying Buildings in Satellite Images with Machine Learning and Quilt -> NDVI & edge detection via gaussian blur as features, fed to TPOT for training with labels from OpenStreetMap, modelled as a two class problem, “Buildings” and “Nature”
Import OpenStreetMap data into Unreal Engine 4
OSMDeepOD -> perform object detection with retinanet
Match Bing Map Aerial Imagery with OpenStreetMap roads
Computer Vision With OpenStreetMap and SpaceNet — A Comparison
url-map -> A tiny web app to create images from OpenStreetMap maps

QGIS

A popular open source alternative to ArcGIS, desktop appication written in python and extended with plugins

QGIS
Create, edit, visualise, analyse and publish geospatial information. Open source alternative to ArcGIS.
Python scripting
Plugins extend the functionality of QGIS - note these are essentially python scripts
Create your own plugins using the QGIS Plugin Builder
DeepLearningTools plugin -> aid training Deep Learning Models
Mapflow.ai plugin -> various models to extract building footprints etc from Maxar imagery
dzetsaka plugin -> classify different kind of vegetation
Coregistration-Qgis-processing -> Qgis processing plugin for image co-registration; projection and pixel alignment based on a target image, uses Arosics
qgis-stac-plugin -> QGIS plugin for reading STAC APIs
buildseg -> a building extraction plugin of QGIS based on ONNX
deep-learning-datasets-maker -> a QGIS plugin to make datasets creation easier for raster and vector data
Modzy-QGIS-Plugin -> demos Vehicle Detection model
kart -> provides modern, open source, distributed version-control for geospatial and tabular datasets
Plugin for Landcover Classification -> capable of implementing machine learning algorithms such as Random forest, SVM and CNN algorithms such as UNET through a simple GUI framework.
[pg_tileserv])(https://github.com/CrunchyData/pg_tileserv) -> A very thin PostGIS-only tile server in Go. Takes in HTTP tile requests, executes SQL, returns MVT tiles.
pg_featureserv -> Lightweight RESTful Geospatial Feature Server for PostGIS in Go
osm-instance-segmentation -> QGIS plugin for finding changes in vector data from orthophotos (i.e. aerial imagery) using tensorflow
Semi-Automatic Classification Plugin -> supervised classification of remote sensing images, providing tools for the download, the preprocessing and postprocessing of images

Parallel procesing with Dask

Dask provides advanced parallelism and distributed out-of-core computation with a dask.dataframe module designed to scale pandas.

Dask works with your favorite PyData libraries to provide performance at scale for the tools you love
Coiled is a managed Dask service. Get started by reading Democratizing Satellite Imagery Analysis with Dask
Dask with PyTorch for large scale image analysis
dask-geopandas -> offers geospatial capabilities of GeoPandas backed by Dask
stackstac -> Turn a STAC catalog into a dask-based xarray
dask-geomodeling -> On-the-fly operations on geographical maps
dask-image -> many SciPy ndimage functions implemented
Detecting Green Roofs in Toronto -> compares deep learning (Mask R-CNN & fast.ai) and classical approach using NDVI scaled on Dask
Analyze terabyte-scale geospatial datasets with Dask and Jupyter on AWS
austin-ml-change-detection-demo -> A change detection demo for the Austin area using a pre-trained PyTorch model scaled with Dask on Planet imagery

Web apps

Flask is often used to serve up a simple web app based on templated HTML files

FastMap -> Flask deployment of deep learning model performing segmentation task on aerial imagery building footprints
Querying Postgres with Python Fastapi Backend and Leaflet-Geoman Frontend
cropcircles -> a purely-client-side web app originally designed for accurately cropping circular center pivot irrigation fields from aerial imagery
django-large-image -> Django endpoints for working with large images for tile serving
Earth Classification API -> Flask based app that serves a CNN model and interfaces with a React and Leaflet front-end
Demo flask map app -> Building Python-based, database-driven web applications (with maps!) using Flask, SQLite, SQLAlchemy and MapBox
Building a Web App for Instance Segmentation using Docker, Flask and Detectron2
greppo -> Build & deploy geospatial applications quick and easy. Read Build a geospatial dashboard in Python using Greppo
localtileserver -> image tile server for viewing geospatial rasters with ipyleaflet, folium, or CesiumJS locally in Jupyter or remotely in Flask applications. Checkout bokeh-tiler
flask-geocoding-webapp -> A quick example Flask application for geocoding and rendering a webmap using Folium/Leaflet
flask-vector-tiles -> A simple Flask/leaflet based webapp for rendering vector tiles from PostGIS
Crash Severity Prediction -> using CAS Open Data and Maxar Satellite Imagery, React app
wildfire-detection-from-satellite-images-ml -> simple flask app for classification
SlumMappingViaRemoteSensingImagery -> learning slum segmentation and localization using satellite imagery and visualising on a flask app
cloud-removal-deploy -> flask app for cloud removal
clearcut_detection -> research & web-service for clearcut detection

Jupyter

The Jupyter Notebook is a web-based interactive computing platform. There are many extensions which make it a powerful environment for analysing satellite imagery

jupyterlite -> JupyterLite is a JupyterLab distribution that runs entirely in the browser
jupyter-splitview -> before/after image sliders in JupyterLab
folium -> display interactive maps in Jupyter notebooks
ipyannotations -> Image annotations in python using jupyter notebooks
pigeonXT -> create custom image classification annotators within Jupyter notebooks
jupyter-innotater -> Inline data annotator for Jupyter notebooks
jupyter-bbox-widget -> A Jupyter widget for annotating images with bounding boxes
mapboxgl-jupyter -> Use Mapbox GL JS to visualize data in a Python Jupyter notebook
pylabel -> includes an image labeling tool that runs in a Jupyter notebook that can annotate images manually or perform automatic labeling using a pre-trained model
jupyterlab-s3-browser -> extension for browsing S3-compatible object storage
papermill -> Parameterize, execute, and analyze notebooks
pretty-jupyter -> Creates dynamic html report from jupyter notebook

Streamlit

Streamlit is an awesome python framework for creating apps with python. Additionally they will host the apps free of charge. Here I list resources which are EO related. Note that a component is an addon which extends Streamlits basic functionality

cogviewer -> Simple Cloud Optimized GeoTIFF viewer
cogcreator -> Simple Cloud Optimized GeoTIFF Creator. Generates COG from GeoTIFF files.
cogvalidator -> Simple Cloud Optimized GeoTIFF validator
streamlit-image-juxtapose -> A simple Streamlit component to compare images in Streamlit apps. Also checkout jupyter-splitview
streamlit-folium -> Streamlit Component for rendering Folium maps
streamlit-keplergl -> Streamlit component for rendering kepler.gl maps
streamlit-light-leaflet -> Streamlit quick & dirty Leaflet component that sends back coordinates on map click
leafmap-streamlit -> various examples showing how to use streamlit to: create a 3D map using Kepler.gl, create a heat map, display a GeoJSON file on a map, and add a colorbar or change the basemap on a map
geemap-apps -> build a multi-page Earth Engine App using streamlit and geemap
streamlit-geospatial -> A multi-page streamlit app for geospatial
geospatial-apps -> A collection of streamlit web apps for geospatial applications
BirdsPyView -> convert images to top-down view and get coordinates of objects
Build a useful web application in Python: Geolocating Photos -> Step by Step tutorial using Streamlit, Exif, and Pandas
Wild fire detection app
dvc-streamlit-example -> how dvc and streamlit can help track model performance during R&D exploration
stacdiscovery -> Simple STAC Catalogs discovery tool
SARveillance -> Sentinel-1 SAR time series analysis for OSINT use
streamlit-template -> A streamlit app template for geospatial applications
streamlit-labelstudio -> A Streamlit component that provides an annotation interface using the LabelStudio Frontend
streamlit-img-label -> a graphical image annotation tool using streamlit. Annotations are saved as XML files in PASCAL VOC format
Streamlit-Authenticator -> A secure authentication module to validate user credentials in a Streamlit application
prettymapp -> Create beautiful maps from OpenStreetMap data in a webapp
mapa-streamlit -> creating 3D-printable models of the earth surface based on mapa
BoulderAreaDetector -> CNN to classify whether a satellite image shows an area would be a good rock climbing spot or not, deployed to streamlit app
streamlit-remotetileserver -> Easily visualize a remote raster given a URL and check if it is a valid Cloud Optimized GeoTiff (COG)
Streamlit_Image_Sorter -> Generic Image Sorter Interface for Streamlit
Streamlit-Folium + Snowflake + OpenStreetMap -> demonstrates the power of Snowflake Geospatial data types and queries combined with Streamlit
observing-earth-from-space-with-streamlit -> blog post on the SatSchool app

Julia language

Julia looks and feels a lot like Python, but can be much faster. Julia can call Python, C, and Fortran libraries and is capabale of C/Fortran speeds. Julia can be used in the familiar Jupyterlab notebook environment

Why you should invest in Julia now, as a Data Scientist
eBook: Introduction to Datascience with Julia
FastAI.jl -> Repository of best practices for deep learning in Julia, inspired by fastai
Flux.jl -> the ML library that doesn't make you tensor. Checkout The Deep Learning with Julia book
GDAL.jl -> Thin Julia wrapper for GDAL
GeoInterface.jl -> A Julia Protocol for Geospatial Data
GeoJSON.jl -> Utilities for working with GeoJSON data
JuliaImages: image processing and machine vision for Julia
Julia_Geospatial -> Examples for a blog series on Geospatial Julia using ArchGDAL
MLJ.jl -> A Julia machine learning framework
Proj4.jl -> Julia wrapper for the PROJ cartographic projections library
Rasters.jl -> types and methods for reading, writing and manipulating rasterized spatial data including GeoTIFF and NetCDF
RemoteS.jl -> Remote sensing data processing
SatelliteToolbox.jl -> This package contains several functions to build simulations related with satellites
SatelliteDynamics.jl -> a satellite dynamics modeling package
Sentinel.jl -> library for processing ESA Sentinel 2 satellite data
DeepSat-Kaggle -> uses Julia

Movers and shakers on Github

Adam Van Etten is doing interesting things in object detection and segmentation
Andrew Cutts cohosts the Scene From Above podcast and has many interesting repos
Ankit Kariryaa published a recent nature paper on tree detection
Chris Holmes is doing great things at Planet
Christoph Rieke maintains a very popular imagery repo and has published his thesis on segmentation
Daniel J Dufour builds geotiff.io and more
Daniel Moraite is publishing some excellent articles
Even Rouault maintains several of the most critical tools in this domain such as GDAL
Fatih Cagatay Akyon aka fcakyon is maintaining sahi and many other useful projects
Gonzalo Mateo García is working on clouds and Water segmentation with CNNs
Isaac Corley is working on super-resolution and torchrs
Jake Shermeyer many interesting repos
Martin Black is tech lead at Sparkgeo in the UK
Maxime Lenormand authors the Minds Behind Maps podcast
Mike Skaug is a Data scientist at Aurora Insight
Mort Canty is an expert in change detection
Mykola Kozyr is working on streamlit apps
Nicholas Murray is an Australia-based scientist with a focus on delivering the science necessary to inform large scale environmental management and conservation
Oscar Mañas is advancing the state of the art in SSL
Qiusheng Wu is an Assistant Professor in the Department of Geography at the University of Tennessee, checkout his YouTube channel
Rodrigo Caye Daudt is doing great work on change detection
Robin Wilson is a former academic who is very active in the satellite imagery space
Rohit Singh has some great Medium articles

Companies & organisations on Github

For a full list of companies, on and off Github, checkout awesome-geospatial-companies. The following lists companies with interesting Github profiles

Airbus Defence And Space
Agricultural Impacts Research Group
Astraea
Applied-GeoSolutions
Azavea -> lots of interesting repos around STAC
CARTO -> "The leading platform for Location Intelligence and Spatial Data Science"
Cervest -> Climate Intelligence
Citymapper
Defense Innovation Unit (DIU) -> run the xView challenges
Development Seed
Descartes Labs
Dymaxion Labs
DHI GRAS
ElementAI
Element 84
ESA-PhiLab
Esri
Geoalert -> checkout their Medium articles
Global Environmental Remote Sensing Laboratory
GIC-AIT -> Aisan Institute of Technology
HSG-AIML -> Artificial Intelligence & Machine Learning (AI:ML Lab) at HSG
Hummingbird Technologies Ltd -> sustainability and optimised food production
ICEYE
indigo-ag
L3Harris Geospatial Solutions (HGSI)
Mapbox -> thanks for Rasterio!
Maxar-Analytics
ml6team
NASA
NASA-JPL -> THE Jet Propulsion Laboratory
NASA Harvest Mission -> NASA’s Food Security and Agriculture Program
National Geospatial-Intelligence Agency USA
Near Space Labs
OpenGeoScales
Open Geospatial Consortium -> the OGC
Ordnance Survey
OroraTech
Planet Labs -> thanks for COGS!
Preligens -> formerly Earthcube Lab
pyronear -> Preserving forests from wildfires one commit at a time
SatelliteVu -> thermal imagery from space!
Sinergise -> maintaining Sentinel-hub
SIAnalytics -> Korean AI firm
SkyTruth
SpaceKnow
Sparkgeo
Tracasa -> created SEN2ROAD
up42 -> Airbus spinout providing 'The easiest way to build geospatial solutions'
Vortexa -> energy & shipping insights

Courses

Introduction to Geospatial Raster and Vector Data with Python -> an intro course on a single page
Manning: Monitoring Changes in Surface Water Using Satellite Image Data
Automating GIS processes includes a lesson on automating raster data processing
For deep learning checkout the fastai course which uses the fastai library & pytorch
pyimagesearch.com hosts courses and plenty of material using opencv and keras
Official opencv courses on opencv.org
TensorFlow Developer Professional Certificate
Geospatial_Python_CourseV1 -> a collection of blog posts turned into a course format
Satellite Machine Learning Training -> lessons on how to apply Machine Learning analysis to satellite data
DL-for-satellite-image-analysis -> short and minimalistic examples covering fundamentals of Deep Learning for Satellite Image Analysis using Jupyter notebooks, created by lakmalnd
Machine Learning on Earth Observation: ML4EO Bootcamp
Artificial Intelligence (AI) for Earth Monitoring
Deep Learning DIY
UvA Deep Learning Tutorials
Practical Data Science Specialization -> AWS specific, develop and scale your data science projects into the cloud using Amazon SageMaker
Disaster Risk Monitoring Using Satellite Imagery by NVIDIA
Course materials for: Geospatial Data Science
Data-Science-For-Beginners -> by Microsoft
AI-For-Beginners -> by Microsoft
Remote Sensing Tutorials -> by the Canada Centre for Mapping and Earth Observation

Books

I highly recommend Deep Learning with Python by François Chollet
Image Analysis, Classification and Change Detection in Remote Sensing With Algorithms for Python, Fourth Edition, By Morton John Canty -> code here
Practical Deep Learning for Cloud, Mobile & Edge
d2l.ai -> Interactive deep learning book with code, math, and discussions
interpretable-ml-book
pyGIS -> an online textbook covering all the core geospatial functionality available in Python
Deep Learning Interviews book
Geographic Data Science with Python -> data science applied to geographic problems and data
eBook: Introduction to Datascience with Julia
Deep Learning In Production Book
Land Use Cover Datasets and Validation Tools
Intermediate Python

Podcasts

Newsletters

Radiant Earth ML4EO market news
A Closer Look with Joe Morrison -> examines the business and technology of mapping
TerraWatch Space by Aravind
Payload Space
Geoscience and Remote Sensing eNewsletter from grss-ieee
Weekly Remote Sensing and Geosciences news by Rafaela Tiengo

Online communities

fast AI geospatial study group
Kaggle Intro to Satellite imagery Analysis group
Omdena brings together small teams of engineers to work on AI projects

Jobs

Signup for the geospatial-jobs-newsletter and Pangeo discourse lists multiple jobs, global. List of job portals below:

Capella Space
Development SEED
Earthdaily
EO-jobs -> List of earth observation companies and job sites
Planet
Satellite Vu
Sparkgeo

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