robmarkcole / satellite-image-deep-learning

среда, 19 октября 2022 г. в 00:37:02

https://github.com/robmarkcole/satellite-image-deep-learning

Deep learning with satellite & aerial imagery

This repository lists resources on the topic of deep learning applied to satellite and aerial imagery. To a lesser extent classical machine learning techniques are listed, as are topics such as cloud computing and model deployment. You can follow me on Twitter and join the dedicated satellite-image-deep-learning group on LinkedIn. If this resource is useful to your work please consider sponsoring it with a donation via Github Sponsors

How to use this repository: if you know exactly what you are looking for (e.g. a dataset name) you can Control+F to search for it in the page. Otherwise refer to the table of contents below, and search within the relevant section. Material that is suitable for getting started with a topic is tagged with the 🔰 emoji, which can also be searched.

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. Read my blog post A brief introduction to satellite image classification with neural networks

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
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
aitlas-arena -> An open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO)
droughtwatch -> code for 2020 paper: Satellite-based Prediction of Forage Conditions for Livestock in Northern Kenya
JSTARS_2020_DPN-HRA -> code for 2020 paper: Deep Prototypical Networks With Hybrid Residual Attention for Hyperspectral Image Classification
SIGNA -> code for 2022 paper: Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification
Satellite Image Classification using rmldnn and Sentinel 2 data

Segmentation

Segmentation will assign a class label to each pixel in an image. Single class models are often trained for road or building segmentation, with multi class for land use/crop type classification. 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. Read my blog post A brief introduction to satellite image segmentation with neural networks

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
segmentation_gym -> A neural gym for training deep learning models to carry out geoscientific image segmentation
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. Also a pytorch implementation here
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
ST-UNet -> code for 2022 paper: Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation
EDFT -> code for 2022 paper: Efficient Depth Fusion Transformer for Aerial Image Semantic Segmentation
WiCoNet -> code for 2022 paper: Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images
CRGNet -> code for 2022 paper: Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations
SA-UNet -> code for 2022 paper: Improved U-Net Remote Sensing Classification Algorithm Fusing Attention and Multiscale Features
MANet -> code for 2020 paper: Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images
BANet -> code for 2021 paper: Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images
MACU-Net -> code for 2022 paper: MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images
DNAS -> code for 2022 paper: DNAS: Decoupling Neural Architecture Search for High-Resolution Remote Sensing Image Semantic Segmentation
A2-FPN -> code for 2021 paper: A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images
MAResU-Net -> code for 2020 paper: Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images
ml_segmentation -> semantic segmentation of buildings using Random Forest, Support Vector Machine (SVM) & Gradient Boosting Classifier (GBC)
RSEN -> code for 2021 paper: Robust Self-Ensembling Network for Hyperspectral Image Classification
MSNet -> code for 2022 paper: MSNet: multispectral semantic segmentation network for remote sensing images
k-textures -> code (R) for 2022 paper: K-textures, a self-supervised hard clustering deep learning algorithm for satellite image segmentation
Swin-Transformer-Semantic-Segmentation -> code for 2021 paper: Satellite Image Semantic Segmentation
UDA_for_RS -> code for 2022 paper: Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer

Segmentation - Land use & land cover

U-Net for Semantic Segmentation on Unbalanced Aerial Imagery -> using the Dubai dataset 🔰
Semantic Segmentation of Dubai dataset Using a TensorFlow U-Net Model 🔰
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
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
LULCMapping-WV3images-CORINE-DLMethods -> code for 2022 paper: Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images
DCSA-Net -> code for 2022 paper: Dynamic Convolution Self-Attention Network for Land-Cover Classification in VHR Remote-Sensing Images
CHeGCN-CNN_enhanced_Heterogeneous_Graph -> code for 2022 paper: CNN-Enhanced Heterogeneous Graph Convolutional Network: Inferring Land Use from Land Cover with a Case Study of Park Segmentation

Segmentation - Vegetation, crops & crop boundaries

Сrор field boundary detection: approaches and main challenges -> Medium article, covering historical and modern approaches 🔰
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
crop-type-detection-ICLR-2020 -> Winning Solutions from Crop Type Detection Competition at CV4A workshop, ICLR 2020
Crop identification using satellite imagery -> Medium article, introduction to crop identification
S4A-Models -> Various experiments on the Sen4AgriNet dataset
attention-mechanism-unet -> code for 2022 paper: An attention-based U-Net for detecting deforestation within satellite sensor imagery
Cocoa_plantations_detection -> Detecting cocoa plantation in Ivory Coast using Sentinel-2 remote sensing data using KNN, SVM, Random Forest and MLP

Segmentation - Water, coastlines & floods

Flood Detection and Analysis using UNET with Resnet-34 as the back bone uses fastai 🔰
Automatic Flood Detection from Satellite Images Using Deep Learning 🔰
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
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
dlsim -> code for 2020 paper: Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping
Water-HRNet -> HRNet trained on Sentinel 2
semantic segmentation model to identify newly developed or flooded land using NAIP imagery provided by the Chesapeake Conservancy, training on MS Azure

Segmentation - Fire, smoke & burn areas

SatelliteVu-AWS-Disaster-Response-Hackathon -> fire spread prediction using classical ML & deep learning 🔰
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
Relict_landslides_CNN_kmeans -> code for 2022 paper: Relict landslide detection in rainforest areas using a combination of k-means clustering algorithm and Deep-Learning semantic segmentation models
Landslide-mapping-on-SAR-data-by-Attention-U-Net -> code for 2022 paper: Rapid Mapping of landslide on SAR data by Attention U-net

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
Antarctic-fracture-detection -> uses UNet with the MODIS Mosaic of Antarctica to detect surface fractures (paper)

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
schisto-vegetation -> code for 2022 paper: Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa

Segmentation - Roads

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

Road detection using semantic segmentation and albumentations for data augmention using the Massachusetts Roads Dataset, U-net & Keras. With code 🔰
ML_EPFL_Project_2 -> U-Net in Pytorch to perform semantic segmentation of roads on satellite images 🔰
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
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
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
Road_detection_model -> code for 2022 paper: Mapping Roads in the Brazilian Amazon with Artificial Intelligence and Sentinel-2
DTnet -> code for 2022 paper: Road detection via a dual-task network based on cross-layer graph fusion modules
Automatic-Road-Extraction-from-Historical-Maps-using-Deep-Learning-Techniques -> code for the paper: Automatic Road Extraction from Historical Maps using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II map
Istanbul_Dataset -> segmentation on the Istanbul, Inria and Massachusetts datasets

Segmentation - Buildings & rooftops

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 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) 🔰
Building footprint detection with fastai on the challenging SpaceNet7 dataset uses U-Net & fastai 🔰
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
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. See the open-buildings dataset
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
CVNet -> code for 2022 paper: CVNet: Contour Vibration Network for Building Extraction
CFENet -> code for 2022 paper: A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery
HiSup -> code for 2022 paper: Accurate Polygonal Mapping of Buildings in Satellite Imagery
BuildingExtraction -> code for 2021 paper: Building Extraction from Remote Sensing Images with Sparse Token Transformers
coseg_building -> code for the 2022 paper: CrossGeoNet: A Framework for Building Footprint Generation of Label-Scarce Geographical Regions
AFM_building -> code for 2021 paper: Building Footprint Generation Through Convolutional Neural Networks With Attraction Field Representation
ramp-code -> repository for the ramp (Replicable AI for MicroPlanning) project
Building-instance-segmentation -> code for 2022 paper: Multi-Modal Feature Fusion Network with Adaptive Center Point Detector for Building Instance Extraction

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 and DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping
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
ood_instances_sophia -> Material for the poster "The effect of PV array instances and images backgrounds on OOD generalization"
photovoltaic-detection -> Detecting available rooftop area from satellite images to install photovoltaic panels

Segmentation - Other manmade

Aarsh2001/ML_Challenge_NRSC -> Electrical Substation detection
electrical_substation_detection -> using UNet, Albumentations for image augmentation, and OpenCV for computer vision tasks
PLGAN-for-Power-Line-Segmentation -> code for 2022 paper: PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images

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 🔰
Building-Detection-MaskRCNN -> Building detection from the SpaceNet dataset by using Mask RCNN 🔰
Oil tank instance segmentation with Mask R-CNN with accompanying article using Keras & Airbus Oil Storage Detection Dataset on Kaggle
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.

Object detection on Satellite Imagery using RetinaNet -> using the Kaggle Swimming Pool and Car Detection dataset 🔰
Tackling the Small Object Problem in Object Detection 🔰
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
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 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
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
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
LO-Det -> code for the 2021 paper: LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images
R2IPoints -> code for 2022 paper: R²IPoints: Pursuing Rotation-Insensitive Point Representation for Aerial Object Detection
Object-Detection -> code for 2022 paper: Multi-Scale Object Detection with the Pixel Attention Mechanism in a Complex Background
mmdet-rfla -> code for 2022 paper: RFLA: Gaussian Receptive based Label Assignment for Tiny Object Detection
Interactive-Multi-Class-Tiny-Object-Detection -> code for 2022 paper: Interactive Multi-Class Tiny-Object Detection
small-object-detection-benchmark -> code for ICIP 2022 paper: Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection

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 🔰
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
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

Orinted bounding boxes (OBB) 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. With paper
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. With paper
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
NPMMR-Det -> code for 2021 paper: A Novel Nonlocal-Aware Pyramid and Multiscale Multitask Refinement Detector for Object Detection in Remote Sensing Images
AOPG -> code for 2022 paper: Anchor-Free Oriented Proposal Generator for Object Detection
SE2-Det -> code for 2022 paper: Semantic-Edge-Supervised Single-Stage Detector for Oriented Object Detection in Remote Sensing Imagery
OrientedRepPoints -> code for 2021 paper: Oriented RepPoints for Aerial Object Detection
TS-Conv -> code for 2022 paper: Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images

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
SuperYOLO -> code for 2022 paper: SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery

Salient object detection

Detecting the most noticeable or important object in a scene

ACCoNet -> code for 2022 paper: 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. With paper
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
GPnet -> code for 2022 paper: Global Perception Network for Salient Object Detection in Remote Sensing Images

Object detection - Buildings, rooftops & solar panels

satellite_image_tinhouse_detector -> Detection of tin houses from satellite/aerial images using the Tensorflow Object Detection API 🔰
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
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

kaggle-ships-in-Google-Earth-yolov5 -> Applying YOLOv5 to Kaggle Ships in Google Earth dataset 🔰
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
DRENet -> code for 2022 [paper])(https://ieeexplore.ieee.org/abstract/document/9791363): A Degraded Reconstruction Enhancement-Based Method for Tiny Ship Detection in Remote Sensing Images With a New Large-Scale Dataset
xView3-The-First-Place-Solution - A winning solution for xView 3 challenge (Vessel detection, classification and length estimation on Sentinetl-1 images). Contains trained models, inference pipeline and training code & configs to reproduce the results.
SARfish -> Ship detection in Sentinel 1 Synthetic Aperture Radar (SAR) imagery

Object detection - Cars, vehicles & trains

Detection of parkinglots and driveways with retinanet 🔰
pytorch-vedai -> object detection on the VEDAI dataset: Vehicle Detection in Aerial Imagery 🔰
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
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
Vehicle-Counting-in-Very-Low-Resolution-Aerial-Images -> code for 2022 paper: Vehicle Counting in Very Low-Resolution Aerial Images via Cross-Resolution Spatial Consistency and Intraresolution Time Continuity

Object detection - Planes & aircraft

rareplanes-yolov5 -> using YOLOv5 and the RarePlanes dataset to detect and classify sub-characteristics of aircraft, with article 🔰
Faster RCNN to detect airplanes 🔰
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
Using Detectron2 to segment aircraft from satellite imagery -> pytorch and Rare Planes
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
HRPlanes -> weights of YOLOv4 and Faster R-CNN networks trained with HRPlanes dataset
aerial-detection -> uses Yolov5 & Icevision

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
MarineMammalsDetection -> Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images
Audubon_F21 -> code for 2022 paper: Deep object detection for waterbird monitoring using aerial imagery

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
PTDM -> code for 2022 paper: Pomelo Tree Detection Method Based on Attention Mechanism and Cross-Layer Feature Fusion
urban-tree-detection -> code for 2022 paper: Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery. With dataset

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. Used in the 2022 paper: Improved YOLOv5 network method for remote sensing image-based ground objects recognition
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

There are many algorithms that use band math to detect clouds, but the deep learning approach is to use semantic segmentation

CloudSEN12 -> Sentinel 2 cloud dataset with a varierty of models here
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
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
MEcGANs -> Cloud Removal from Satellite Imagery using Multispectral Edge-filtered Conditional Generative Adversarial Networks
CloudXNet -> code for 2020 paper: CloudX-net: A robust encoder-decoder architecture for cloud detection from satellite remote sensing images
refined-unet-lite -> code for 2022 paper: Refined UNet Lite: End-to-End Lightweight Network for Edge-precise Cloud Detection
cloud-buster -> Sentinel-2 L1C and L2A Imagery with Fewer Clouds
SatelliteCloudGenerator -> A PyTorch-based tool to generate clouds for satellite images
SEnSeI -> A python 3 package for developing sensor independent deep learning models for cloud masking in satellite imagery

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 -> code for 2022 paper: Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
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_Vulcan -> Damage assessment using pre and post orthoimagery. Modified + productionized model based off the first-place model from the xView2 challenge.
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.
IRCNN -> code for 2022 paper: IRCNN: An Irregular-Time-Distanced Recurrent Convolutional Neural Network for Change Detection in Satellite Time Series
UTRNet -> An Unsupervised Time-Distance-Guided Convolutional Recurrent Network for Change Detection in Irregularly Collected Images
open-cd -> an open source change detection toolbox based on a series of open source general vision task tools
Tiny_model_4_CD -> code for 2022 paper: TINYCD: A (Not So) Deep Learning Model For Change Detection. Uses LEVIR-CD & WHU-CD datasets
FHD -> code for 2022 paper: Feature Hierarchical Differentiation for Remote Sensing Image Change Detection
Change detection with Raster Vision -> blog post with Colab notebook
building-expansion -> code for 2021 paper: Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms
SaDL_CD -> code for 2022 paper: Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection
EGCTNet_pytorch -> code for 2022 paper: Building Change Detection Based on an Edge-Guided Convolutional Neural Network Combined with a Transformer
S2-cGAN -> code for 2020 paper: S2-cGAN: Self-Supervised Adversarial Representation Learning for Binary Change Detection in Multispectral Images

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
satellite_image_forecasting -> predict future satellite images from past ones using features such as precipitation and elevation maps. Entry for the EarthNet2021 challenge

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
SPACY -> Satellite Prediction of Aggregate Corn Yield
cropyieldArticle -> code for 2022 paper: Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

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
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
Satellite Image Analysis with fast.ai for Disaster Recovery
shackleton -> leverages remote sensing imagery and machine learning techniques to provide insights into various transportation and evacuation scenarios in an interactive dashboard that conducts real-time computation

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
RRSGAN -> code for 2021 paper: RRSGAN: Reference-Based Super-Resolution for Remote Sensing Image
HDR-DSP-SR -> code for 2021 paper: Self-supervised multi-image super-resolution for push-frame satellite images

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. With paper
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
pixel-smasher -> code for 2020 paper: Super-Resolution Surface Water Mapping on the Canadian Shield Using Planet CubeSat Images and a Generative Adversarial Network

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. With paper
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
worldstrat -> SISR and MISR implementations of SRCNN
MISR-GRU -> Pytorch implementation of MISR-GRU, a deep neural network for multi image super-resolution (MISR), for ProbaV Super Resolution Competition

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
S2_SSC_CNN -> code for 2020 paper: Zero-shot Sentinel-2 Sharpening Using A Symmetric Skipped Connection Convolutional Neural Network
S2S_UCNN -> code for 2021 paper: Sentinel 2 sharpening using a single unsupervised convolutional neural network with MTF-Based degradation model
SSE-Net -> code for 2022 paper: Spatial and Spectral Extraction Network With Adaptive Feature Fusion for Pansharpening
UCGAN -> code for 2022 paper: Unsupervised Cycle-consistent Generative Adversarial Networks for Pan-sharpening
GCPNet -> code for 2022 paper: When Pansharpening Meets Graph Convolution Network and Knowledge Distillation
PanFormer -> code for 2022 paper: PanFormer: a Transformer Based Model for Pan-sharpening
Pansharpening -> code for 2021 paper: Pansformers: Transformer-Based Self-Attention Network for 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
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
Satellite Image Augmetation with GANs -> code for 2022 paper: Image Augmentation for Satellite Images

Transformers

Transformer-in-Remote-Sensing -> code for 2022 paper: Transformers in Remote Sensing: A Survey
Transformers in remote sensing -> blog post by robmarkcole
Remote-Sensing-RVSA -> code for 2022 paper: Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model
SatViT -> self-supervised training of multispectral optical and SAR vision transformers
UDA_for_RS -> code for 2022 paper: Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer

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
SACNet -> code for 2021 paper: Self-Attention Context Network: Addressing the Threat of Adversarial Attacks for Hyperspectral Image 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
MetaCaptioning -> code for 2022 paper: Meta captioning: A meta learning based remote sensing image captioning framework
Transformer-for-image-captioning -> a transformer for image captioning, trained on the UCM dataset

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
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
TIP_2022_CMFSL -> code for 2022 paper: Few-shot Learning with Class-Covariance Metric for Hyperspectral Image Classification
sen12ms-human-few-shot-classifier -> code for paper: Humans are poor few-shot classifiers for Sentinel-2 land cover
S3Net -> code for 2022 paper: S3Net: Spectral–Spatial Siamese Network for Few-Shot Hyperspectral Image Classification
SiameseNet-for-few-shot-Hyperspectral-Classification -> code for 2020 paper: 3DCSN:SiameseNet-for-few-shot-Hyperspectral-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
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
vissl_experiments -> Self-supervised Learning using Facebook VISSL on the RESISC-45 satellite imagery classification dataset
MS2A-Net -> code for 2022 paper: MS 2 A-Net: Multi-scale spectral-spatial association network for hyperspectral image clustering
UDA_for_RS -> code for paper: Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer
MERLIN -> code for 2021 paper: As if by magic: self-supervised training of deep despeckling networks with MERLIN

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
ICSS -> code for 2022 paper: Weakly-supervised continual learning for class-incremental segmentation
es-CP -> code for 2022 paper: Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework

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
DIAL -> code for 2022 paper: DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing

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
L2_Siamese -> code for the 2020 paper: Registration of Multiresolution Remote Sensing Images Based on L2-Siamese Model

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
HSHT-Satellite-Imagery-Synthesis -> code for thesis - Improving Flood Maps by Increasing the Temporal Resolution of Satellites Using Hybrid Sensor Fusion
MDC -> code for 2021 paper: Unsupervised Data Fusion With Deeper Perspective: A Novel Multisensor Deep Clustering Algorithm
FusAtNet -> code for 2020 paper: FusAtNet: Dual Attention based SpectroSpatial Multimodal Fusion Network for Hyperspectral and LiDAR Classification
AMM-FuseNet -> code for 2022 paper: AMM-FuseNet: Attention-Based Multi-Modal Image Fusion Network for Land Cover Mapping
S1-S2_Transformer -> Sentinel-1 SAR and Sentinel-2 optical timeseries based Transformer architecture for tropical dry forest disturbance mapping
MANet -> code for 2022 paper: MANet: A Network Architecture for Remote Sensing Spatiotemporal Fusion Based on Multiscale and Attention Mechanisms
DCSA-Net -> code for 2022 paper: Dynamic Convolution Self-Attention Network for Land-Cover Classification in VHR Remote-Sensing Images

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
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
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
Photogrammetry-Guide -> A guide covering Photogrammetry including the applications, libraries and tools that will make you a better and more efficient Photogrammetry development
DSM-to-DTM -> Exploring the use of machine learning to convert a Digital Surface Model (e.g. SRTM) to a Digital Terrain Model
GF-7_Stereo_Matching -> code for paper: Large Scene DSM Generation of Gaofen-7 Imagery Combined with Deep Learning
Mapping drainage ditches in forested landscapes using deep learning and aerial laser scanning

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
LEO-GEO-landsurfacetemp -> Multi-sensor machine learning to retrieve high spatiotemporal resolution land surface temperature

SAR

See SAR

NDVI - 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 NDVI 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
Hyperspectral-Deblurring-and-Destriping
HyDe -> Hyperspectral Denoising algorithm toolbox in Python, with paper
HLF-DIP -> code for 2022 paper: Unsupervised Hyperspectral Denoising Based on Deep Image Prior and Least Favorable Distribution
RQUNetVAE -> code for 2022 paper: Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising
deep-hs-prior -> code for 2019 paper: Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolution
iquaflow -> from Satellogic, an image quality framework that aims at providing a set of tools to assess image quality by using the performance of AI models trained on the images as a proxy.

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

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 -> a library for creating machine-learning ready data by pairing satellite images with OpenStreetMap (OSM) vector 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
srtmDownloader -> Python library (multi-threaded) for retrieving SRTM elevation map of CGIAR-CSI
ImageDatasetViz -> create a mosaic of images in a dataset for previewing purposes
landsatlinks -> A simple CLI interface to generate download urls for Landsat Collection 2 Level 1 product bundles
pyeo -> a set of portable, extensible and modular Python scripts for machine learning in earth observation and GIS, including downloading, preprocessing, creation of base layers, classification and validation.
metaearth -> Download and access remote sensing data from any platform
geoget -> Download geodata for anywhere in Earth via ladsweb.modaps.eosdis.nasa.gov
geeml -> A python package to extract Google Earth Engine data for machine learning

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
ImageTilingUtils -> Minimalistic set of image reader agnostic tools to easily iterate over large images
split_raster -> Creates a tiled output from an input raster dataset. pip installable
SAHI -> Utilties for slicing COCO formatted annotations and image files, performing sliced inference using MMDetection, Detectron2, YOLOv5, HuggingFace detectors and calculating AP over image slices.

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

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
Flood-Annotation-Tool -> Annotation tool to annotate flooded and non-flooded regions on satellite image, uses jupyter notebook

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. 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
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
image_sorter -> A quick interface for sorting a folder of images into two other folders
bulk -> A Simple Bulk Labelling Tool using embeddings

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! Read Nearest Neighbor Embeddings Search with Qdrant and FiftyOne
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
mask-to-polygons -> Routines for extracting and working with polygons from semantic segmentation masks
labelme2coco -> Converts LabelMe JSON format into COCO object detection and instance segmentation format.

Training models

This section discusses training machine learning models.

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
mAP@0.5 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
Metrics to Evaluate your Semantic Segmentation Model

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
How To Normalize Satellite Images for Deep Learning
ML Tooling 2022 by developmentseed

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

Others

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

Deep learning projects & frameworks

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 -> Python library for slicing image datasets, performing sliced inference with MMDetection, Detectron2, YOLOv5, Torchvision detectors and generating error analysis plots. Read the arxiv paper and article SAHI: A vision library for large-scale object detection & instance segmentation
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
aitlas-arena -> An open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO)
PaddleRS -> remote sensing image processing development kit
RocketML Deep Neural Networks -> read Satellite Image Classification using rmldnn and Sentinel 2 data
segmentation_gym -> A neural gym for training deep learning models to carry out geoscientific image segmentation

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
Spiral Blue -> startup building edge computers to run AI analytics on-board satellites
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

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
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
RUS Copernicus Training
Practical Deep Learning for Coders -> the popular course by Jeremy Howard using fast.ai
engn3903 -> Environmental Sensing, Mapping and Modelling
PyTorch Fundamentals -> 4 hour course by Microsoft
pytorch-deep-learning -> Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course

Deploying models

See Deployment

Software

See Software

Datasets

See Datasets