Introduction

Deep learning has revolutionized the analysis and interpretation of satellite and aerial imagery, addressing unique challenges such as vast image sizes and a wide array of object classes. This repository provides an exhaustive overview of deep learning techniques specifically tailored for satellite and aerial image processing. It covers a range of architectures, models, and algorithms suited for key tasks like classification, segmentation, and object detection.

How to use this repository: use Command + F (Mac) or CTRL + F (Windows) to search this page for e.g. 'SAM'

Techniques

• Classification
• Segmentation
• Object detection
• Regression
• Cloud detection & removal
• Change detection
• Time series
• Crop classification
• Crop yield & vegetation forecasting
• Generative networks
• Autoencoders, dimensionality reduction, image embeddings & similarity search
• Few & zero shot learning
• Self-supervised, unsupervised & contrastive learning
• SAR
• Explainable Ai (XAI)
• Large vision & language models (LLMs & LVMs)
• Foundational models

Classification

The UC merced dataset is a well known classification dataset.

Classification is a fundamental task in remote sensing data analysis, where the goal is to assign a semantic label to each image, such as 'urban', 'forest', 'agricultural land', etc. The process of assigning labels to an image is known as image-level classification. However, in some cases, a single image might contain multiple different land cover types, such as a forest with a river running through it, or a city with both residential and commercial areas. In these cases, image-level classification becomes more complex and involves assigning multiple labels to a single image. This can be accomplished using a combination of feature extraction and machine learning algorithms to accurately identify the different land cover types. It is important to note that image-level classification should not be confused with pixel-level classification, also known as semantic segmentation. While image-level classification assigns a single label to an entire image, semantic segmentation assigns a label to each individual pixel in an image, resulting in a highly detailed and accurate representation of the land cover types in an image. Read A brief introduction to satellite image classification with neural networks

• EuroSat-Satellite-CNN-and-ResNet -> Classifying custom image datasets by creating Convolutional Neural Networks and Residual Networks from scratch with PyTorch

• Land-Cover-Classification-using-Sentinel-2-Dataset -> well written Medium article accompanying this repo but using the EuroSAT dataset

• 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

• RSI-CB -> A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data. See also Remote-sensing-image-classification

• 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

• SSTN -> Spectral-Spatial Transformer Network for Hyperspectral Image Classification: A FAS Framework

• SatellitePollutionCNN -> A novel algorithm to predict air pollution levels with state-of-the-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

• 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

• ISPRS_S2FL -> Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model

• ensemble_LCLU -> Deep neural network ensembles for remote sensing land cover and land use classification

• Urban-Analysis-Using-Satellite-Imagery -> classify urban area as planned or unplanned using a combination of segmentation and classification

• mining-discovery-with-deep-learning -> Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning

• sentinel2-deep-learning -> Novel Training Methodologies for Land Classification of Sentinel-2 Imagery

• Pay-More-Attention -> Remote Sensing Image Scene Classification Based on an Enhanced Attention Module

• Remote Sensing Image Classification via Improved Cross-Entropy Loss and Transfer Learning Strategy Based on Deep Convolutional Neural Networks

• SKAL -> Looking Closer at the Scene: Multiscale Representation Learning for Remote Sensing Image Scene Classification

• SAFF -> Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification

• GLNET -> 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

• OBIC-GCN -> 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 -> Satellite-based Prediction of Forage Conditions for Livestock in Northern Kenya

• JSTARS_2020_DPN-HRA -> Deep Prototypical Networks With Hybrid Residual Attention for Hyperspectral Image Classification

• SIGNA -> Semantic Interleaving Global Channel Attention for Multilabel Remote Sensing Image Classification

• PBDL -> Patch-Based Discriminative Learning for Remote Sensing Scene Classification

• EmergencyNet -> identify fire and other emergencies from a drone

• satellite-deforestation -> Using Satellite Imagery to Identify the Leading Indicators of Deforestation, applied to the Kaggle Challenge Understanding the Amazon from Space

• RSMLC -> Deep Network Architectures as Feature Extractors for Multi-Label Classification of Remote Sensing Images

• FireRisk -> A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning

• flood_susceptibility_mapping -> Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany

• Building-detection-and-roof-type-recognition -> A CNN-Based Approach for Automatic Building Detection and Recognition of Roof Types Using a Single Aerial Image

• SNN4Space -> project which investigates the feasibility of deploying spiking neural networks (SNN) in land cover and land use classification tasks

• vessel-classification -> classify vessels and identify fishing behavior based on AIS data

• RSMamba -> Remote Sensing Image Classification with State Space Model

• BirdSAT -> Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping

• EGNNA_WND -> Estimating the presence of the West Nile Disease employing Graph Neural network

• cyfi -> Estimate cyanobacteria density based on Sentinel-2 satellite imagery

• 3DGAN-ViT -> A deep learning framework based on generative adversarial networks and vision transformer for complex wetland classification

• automatic_solar_pv_detection -> Automatic Solar PV Panel Image Classification with Deep Neural Network Transfer Learning

• U-netR -> Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery paper

• nshaud/DeepNetsForEO -> Deep networks for Earth Observation with PyTorch implementations of state-of-the-art architectures for remote sensing image classification

• sentinel-landslide-cls -> Classification for Landslide Detection, using Sentinel-1 and Sentinel-2 data.

• Infra-Bench CLS -> code for paper: Infra-Bench CLS: A Global, Open-Source Benchmark for Critical Infrastructure Classification with Earth Observation Foundation Models

• Detecting old-growth forests -> code for paper: Geospatial embeddings detect old-growth forests but buffered spatial validation narrows their advantage over Sentinel features

Segmentation

(left) a satellite image and (right) the semantic classes in the image.

Image segmentation is a crucial step in image analysis and computer vision, with the goal of dividing an image into semantically meaningful segments or regions. The process of image segmentation assigns a class label to each pixel in an image, effectively transforming an image from a 2D grid of pixels into a 2D grid of pixels with assigned class labels. One common application of image segmentation is road or building segmentation, where the goal is to identify and separate roads and buildings from other features within an image. To accomplish this task, single class models are often trained to differentiate between roads and background, or buildings and background. These models are designed to recognize specific features, such as color, texture, and shape, that are characteristic of roads or buildings, and use this information to assign class labels to the pixels in an image. Another common application of image segmentation is land use or crop type classification, where the goal is to identify and map different land cover types within an image. In this case, multi-class models are typically used to recognize and differentiate between multiple classes within an image, such as forests, urban areas, and agricultural land. These models are capable of recognizing complex relationships between different land cover types, allowing for a more comprehensive understanding of the image content. Read A brief introduction to satellite image segmentation with neural networks. Note that many articles which refer to 'hyperspectral land classification' are often actually describing semantic segmentation.

Segmentation - Land use & land cover

• Automatic Detection of Landfill Using Deep Learning

• CDL-Segmentation -> Deep Learning Based Land Cover and Crop Type Classification: A Comparative Study. Compares UNet, SegNet & DeepLabv3+

• LoveDA -> A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

• DeepGlobe Land Cover Classification Challenge solution

• CNN_Enhanced_GCN -> 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

• MCANet -> A joint semantic segmentation framework of optical and SAR images for land use classification. Uses WHU-OPT-SAR-dataset

• land-cover -> Model Generalization in Deep Learning Applications for Land Cover Mapping

• generalizablersc -> Cross-dataset Learning for Generalizable Land Use Scene Classification

• SSLTransformerRS -> Self-supervised Vision Transformers for Land-cover Segmentation and Classification

• LULCMapping-WV3images-CORINE-DLMethods -> Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

• DCSA-Net -> Dynamic Convolution Self-Attention Network for Land-Cover Classification in VHR Remote-Sensing Images

• CHeGCN-CNN_enhanced_Heterogeneous_Graph -> CNN-Enhanced Heterogeneous Graph Convolutional Network: Inferring Land Use from Land Cover with a Case Study of Park Segmentation

• TCSVT_2022_DGSSC -> DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery

• DeepForest-Wetland-Paper -> Deep Forest classifier for wetland mapping using the combination of Sentinel-1 and Sentinel-2 data, GIScience & Remote Sensing

• Wetland_UNet -> UNet models that can delineate wetlands using remote sensing data input including bands from Sentinel-2 LiDAR and geomorphons. By the Conservation Innovation Center of Chesapeake Conservancy and Defenders of Wildlife

• DPA -> DPA is an unsupervised domain adaptation (UDA) method applied to different satellite images for large-scale land cover mapping.

• dynamicworld -> Dynamic World, global 10 m land use land cover mapping from Google. dynamic_world_pytorch is a pytorch implementation.

• spada -> Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery

• M3SPADA -> Multi-Sensor Temporal Unsupervised Domain Adaptation for Land Cover Mapping with spatial pseudo labelling and adversarial learning

• GLNet -> Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images

• LoveNAS -> LoveNAS: Towards Multi-Scene Land-Cover Mapping via Hierarchical Searching Adaptive Network

• FLAIR-2 challenge -> Semantic segmentation and domain adaptation challenge proposed by the French National Institute of Geographical and Forest Information (IGN)

• flair-2 8th place solution

• igarss-spada -> Dataset and code for the paper Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery IGARSS 2023

• cnn-land-cover-eco -> Multi-stage semantic segmentation of land cover in the Peak District using high-resolution RGB aerial imagery

• LALE -> a lightweight hybrid ConvMixer-transformer architecture for efficient land-cover segmentation in remote sensing imagery

Segmentation - Vegetation, deforestation, crops & field boundaries

Note that deforestation detection may be treated as a segmentation task or a change detection task

• DetecTree -> Tree detection from aerial imagery in Python, a LightGBM classifier of tree/non-tree pixels from aerial imagery

• 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

• Tree species classification from from airborne LiDAR and hyperspectral data using 3D convolutional neural networks

• Find sports fields using Mask R-CNN and overlay on open-street-map

• An LSTM to generate a crop mask for Togo

• DeepSatModels -> Context-self contrastive pretraining for crop type semantic segmentation

• 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

• 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

• ResUnet-a -> a deep learning framework for semantic segmentation of remotely sensed data

• DSD_paper_2020 -> 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 -> Forest mapping and monitoring of the African continent using Sentinel-2 data and deep learning

• global-cropland-mapping -> global multi-temporal cropland mapping

• Landuse_DL -> delineate landforms due to the thawing of ice-rich permafrost

• canopy -> A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery

• forest_change_detection -> forest change segmentation with time-dependent models, including Siamese, UNet-LSTM, UNet-diff, UNet3D models

• cultionet -> segmentation of cultivated land, built on PyTorch Geometric and PyTorch Lightning

• sentinel-tree-cover -> 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

• S4A-Models -> Various experiments on the Sen4AgriNet dataset

• attention-mechanism-unet -> An attention-based U-Net for detecting deforestation within satellite sensor imagery

• SummerCrop_Deeplearning -> A Transferable Learning Classification Model and Carbon Sequestration Estimation of Crops in Farmland Ecosystem

• 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

• PTDM -> Pomelo Tree Detection Method Based on Attention Mechanism and Cross-Layer Feature Fusion

• urban-tree-detection -> Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery. With dataset

• BioMassters_baseline -> a basic pytorch lightning baseline using a UNet for getting started with the BioMassters challenge (biomass estimation)

• Biomassters winners -> top 3 solutions

• kbrodt biomassters solution -> 1st place solution

• biomass-estimation -> from Azavea, applied to Sentinel 1 & 2

• 3DUNetGSFormer -> A deep learning pipeline for complex wetland mapping using generative adversarial networks and Swin transformer

• SEANet_torch -> Using a semantic edge-aware multi-task neural network to delineate agricultural parcels from remote sensing images

• arborizer -> Tree crowns segmentation and classification

• ReUse -> REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation

• unet-sentinel -> UNet to handle Sentinel-1 SAR images to identify deforestation

• MaskedSST -> Masked Vision Transformers for Hyperspectral Image Classification

• UNet-defmapping -> master's thesis using UNet to map deforestation using Sentinel-2 Level 2A images, applied to Amazon and Atlantic Rainforest dataset

• cvpr-multiearth-deforestation-segmentation -> multimodal Unet entry to the CVPR Multiearth 2023 deforestation challenge

• TransUNetplus2 -> TransU-Net++: Rethinking attention gated TransU-Net for deforestation mapping. Uses the Amazon and Atlantic forest dataset

• A high-resolution canopy height model of the Earth -> A high-resolution canopy height model of the Earth

• Radiant Earth Spot the Crop Challenge -> Winning models from the Radiant Earth Spot the Crop Challenge, uses a time-series of Sentinel-2 multispectral data to classify crops in the Western Cape of South Africa. Another solution

• transfer-field-delineation -> Multi-Region Transfer Learning for Segmentation of Crop Field Boundaries in Satellite Images with Limited Labels

• crop-field-segmentation-ukan -> KANs and Sentinel for Effective and Explainable Crop Field Segmentation

• mowing-detection -> Automatic detection of mowing and grazing from Sentinel images

• PTAViT3D and PTAViT3DCA -> Tackling fluffy clouds: field boundaries detection using time series of S2 and/or S1 imagery

• ai4boundaries -> a Python package that facilitates download of the AI4boundaries data set

• Nasa_harvest_field_boundary_competition -> Nasa Harvest Rwanda Field Boundary Detection Challenge Tutorial

• UTB_codes -> The Urban Tree Canopy Cover in Brazil article

• nasa_harvest_boundary_detection_challenge -> the 4th place solution for NASA Harvest Field Boundary Detection Challenge on Zindi.

• rainforest-segmentation -> Identifying and tracking deforestation in the Amazon Rainforest using state-of-the-art deep learning models and multispectral satellite imagery.

• Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery

• Semantic_segmentation_for_LCLUC -> Semantic Segmentation for Simultaneous Crop and Land Cover Land Use Classification Using Multi-Temporal Landsat Imagery

• boundary-sam -> parcel boundary delineation using SAM, image embeddings and detail enhancement filters

• TOFMapper -> a semantic segmentation tool for mapping and classifying Trees outside Forest in high resolution aerial images

• Mask-PSTIN -> Improving crop type mapping by integrating LSTM with temporal random masking and pixel-set spatial information

• paddy_identification -> Paddy Field Instance Segmentation using Multi-Temporal SAR Time Series

• CropSight -> towards a large-scale operational framework for object-based crop type ground truth retrieval using street view and PlanetScope satellite imagery

• ftw-prue -> PRUE: A Practical Recipe for Field Boundary Segmentation at Scale.

• agribound -> An AI-powered field boundary delineation toolkit combining satellite foundation models, embeddings, and global training data for accurate agricultural parcel/field boundary mapping.

• s2-forest-browning-monitoring -> Monitoring forest browning using Sentinel-2 imagery.

• LacunaLabels -> A region-wide, multi-year set of crop field boundary labels for Africa.

• Pseudo-fields -> Generating pseudo labels for satellite-based crop field delineatio.

• JEDI -> code for paper: JEDI: JEPA-to-Edge Distillation for Efficient Cropland Segmentation from Satellite Imagery

• NAIP Farmland ResSAM -> code for paper: Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement

• Fields of the Planet -> code and dataset for paper: Fields of the Planet: Field Boundary Mapping Beyond 10m

Segmentation - Water, coastlines, rivers & floods

• sat-water -> Semantic segmentation of water bodies in satellite imagery, producing pixel-wise water masks from remote sensing images using a U-Net–style deep learning pipeline (data preparation, training, inference, and evaluation).

• 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

• floodmaps -> an end-to-end pipeline and segmentation models for flood-water detection using Sentinel-1 SAR and Sentinel-2 multispectral imagery

• 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

• 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

• floatingobjects -> TOWARDS DETECTING FLOATING OBJECTS ON A GLOBAL SCALE WITHLEARNED SPATIAL FEATURES USING SENTINEL 2. Uses U-Net & pytorch

• SpaceNet8 -> baseline Unet solution to detect flooded roads and buildings

• dlsim -> 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

• BandNet -> Analysis and application of multispectral data for water segmentation using machine learning. Uses Sentinel-2 data

• mmflood -> MMFlood: A Multimodal Dataset for Flood Delineation From Satellite Imagery (Sentinel 1 SAR)

• Urban_flooding -> Towards transferable data-driven models to predict urban pluvial flood water depth in Berlin, Germany

• MECNet -> Rich CNN features for water-body segmentation from very high resolution aerial and satellite imagery

• SWRNET -> A Deep Learning Approach for Small Surface Water Area Recognition Onboard Satellite

• elwha-segmentation -> fine-tuning Meta's Segment Anything (SAM) for bird's eye view river pixel segmentation

• RiverSnap -> code for paper: A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery

• SAR-water-segmentation -> Deep Learning based Water Segmentation Using KOMPSAT-5 SAR Images

• TerraMind-Flood -> DEM-Enhanced Flood Detection with Physics-Aware Learning, applied to Sen1Flood11

• Prithvi-CAFE -> Transformer-based global reasoning (Prithvi-EO-2.0) with CNN-based local spatial sensitivity, enabling high-resolution, reliable flood inundation mapping across multi-channel/sensor inputs, applied to Sen1Flood11

• SMAGNet -> A Spatially Masked Adaptive Gated Network for Multimodal Post-Flood Water Extent Mapping using SAR and Incomplete Multispectral Data. Uses c2smsfloods dataset

• IBM BlueSky Challenge - ZeroFlood

• OmniWaterMask-training -> Training code for the deep learning model used in OmniWaterMask - a Python library for detecting water bodies in satellite and aerial imagery.

• utae-water-segmentation -> UTAE-PAPS model for water/land segmentation using Sentinel-1 and Sentinel-2 data with IBM Granite flood detection dataset

Segmentation - Fire, smoke & burn areas

• SatelliteVu-AWS-Disaster-Response-Hackathon -> fire spread prediction using classical ML & deep learning

• A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS

• IndustrialSmokePlumeDetection -> using Sentinel-2 & a modified ResNet-50

• burned-area-detection -> uses Sentinel-2

• rescue -> Attention to fires: multi-channel deep-learning models for wildfire 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

• burned-area-baseline -> baseline unet model accompanying the Satellite Burned Area Dataset (Sentinel 1 & 2)

• burned-area-seg -> Burned area segmentation from Sentinel-2 using multi-task learning

• chabud2023 -> Change detection for Burned area Delineation (ChaBuD) ECML/PKDD 2023 challenge

• Post Wildfire Burnt-up Detection using Siamese-UNet -> on Chadbud dataset

• vit-burned-detection -> Vision transformers in burned area delineation

• ai4good25-wildfire -> AI4GOOD Class Fall 2025 : Wildfire spread prediction project

• wildfire-lora-gfm -> adapting large Earth-Observation foundation models (Prithvi-v2, TerraMind, DINOv3) using LoRA, to detect wildfire burned areas from bi-temporal (pre-fire / post-fire) Sentinel-2 imagery.

Segmentation - Landslides

• landslide-sar-unet -> Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes

• landslide-mapping-with-cnn -> A new strategy to map landslides with a generalized convolutional neural network

• Landslide-mapping-on-SAR-data-by-Attention-U-Net -> Rapid Mapping of landslide on SAR data by Attention U-net

• SAR-landslide-detection-pretraining -> SAR-based landslide classification pretraining leads to better segmentation

• Landslide mapping from Sentinel-2 imagery through change detection

• landslide4sense-solution -> solution of Tek Kshetri

• DiGATe-UNet-LandSlide-Segmentation -> Lightweight Dual-Stream Framework for Landslide Segmentation

• Erosion-detection -> using Sentinel-2 to detect erosion

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

• GlacierSemanticSegmentation

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

• sentinel_lakeice -> Lake Ice Detection from Sentinel-1 SAR with Deep Learning

• MCD-Net -> a lightweight deep learning framework for optical-only moraine segmentation

• landslides_segmentation -> super-resolution and segmentation of multispectral Sentinel-2 satellite imagery, applied to landslide monitoring in Italian municipalities.

• GlacierCastAI -> forecasts glacier boundary retreat from Landsat time series, ERA5 climate data and Copernicus DEM terrain features using a multimodal ConvLSTM model

Segmentation - methane

• Methane-detection-from-hyperspectral-imagery -> Deep Remote Sensing Methods for Methane Detection in Overhead Hyperspectral Imagery

• methane-emission-project -> Classification CNNs was combined in an ensemble approach with traditional methods on tabular data

• CH4Net -> A fast, simple model for detection of methane plumes using sentinel-2

• STARCOP: Semantic Segmentation of Methane Plumes with Hyperspectral Machine Learning models

• Project-Eucalyptus -> pipelines for satellite-based methane detection. Includes trained segmentation models, a synthetic plume generator, and benchmarking tools for Sentinel-2, Landsat 8/9, and EMIT.

• plume-hunter: Towards Methane Detection On Board Satellites

• MethaneFuse -> code for paper: MethaneFuse: Learning from Multi-Sensor Satellite Observations for Methane Plume Detection

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

• EddyNet -> A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies

• schisto-vegetation -> Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa

• Earthformer -> Exploring space-time transformers for earth system forecasting

• weather4cast-2022 -> Unet-3D baseline model for Weather4cast Rain Movie Prediction competition

• WeatherFusionNet -> Predicting Precipitation from Satellite Data. weather4cast-2022 1st place solution

• marinedebrisdetector -> Large-scale Detection of Marine Debris in Coastal Areas with Sentinel-2

• kaggle-identify-contrails-4th -> 4th place Solution, Google Research - Identify Contrails to Reduce Global Warming

• MineSegSAT -> An automated system to evaluate mining disturbed area extents from Sentinel-2 imagery

• asos -> Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery

• SinkSAM -> Knowledge-Driven Self-Supervised Sinkhole Segmentation Using Topographic Priors and Segment Anything Model

• SENSE -> Satellite-based ENergy Synthesis for Sustainable Environment

Segmentation - Roads & sidewalks

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

• ChesapeakeRSC -> segmentation to extract roads from the background but are additionally evaluated by how they perform on the "Tree Canopy Over Road" class

• ML_EPFL_Project_2 -> U-Net in Pytorch to perform semantic segmentation of roads on satellite images

• Winning Solutions from SpaceNet Road Detection and Routing Challenge

• 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)

• 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

• Satellite Imagery Road Segmentation -> intro article on Medium using the kaggle Massachusetts Roads Dataset

• Label-Pixels -> for semantic segmentation of roads and other features

• Satellite-image-road-extraction -> Road Extraction by Deep Residual U-Net

• road_building_extraction -> Pytorch implementation of U-Net architecture for road and building extraction

• RCFSNet -> Road Extraction From Satellite Imagery by Road Context and Full-Stage Feature

• SGCN -> Split Depth-Wise Separable Graph-Convolution Network for Road Extraction in Complex Environments From High-Resolution Remote-Sensing Images

• ASPN -> Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks

• cresi -> Road network extraction from satellite imagery, with speed and travel time estimates

• D-LinkNet -> LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction

• Sat2Graph -> Road Graph Extraction through Graph-Tensor Encoding

• RoadTracer-M -> Road Network Extraction from Satellite Images Using CNN Based Segmentation and Tracing

• ScRoadExtractor -> Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images

• RoadDA -> Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images

• DeepSegmentor -> A Pytorch implementation of DeepCrack and RoadNet projects

• Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images

• NL-LinkNet -> Toward Lighter but More Accurate Road Extraction with Non-Local Operations

• IRSR-net -> 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 -> Mapping Roads in the Brazilian Amazon with Artificial Intelligence and Sentinel-2

• DTnet -> Road detection via a dual-task network based on cross-layer graph fusion modules

• Automatic-Road-Extraction-from-Historical-Maps-using-Deep-Learning-Techniques -> Automatic Road Extraction from Historical Maps using Deep Learning Techniques

• Istanbul_Dataset -> segmentation on the Istanbul, Inria and Massachusetts datasets

• D-LinkNet -> 1st place solution in DeepGlobe Road Extraction Challenge

• PaRK-Detect -> PaRK-Detect: Towards Efficient Multi-Task Satellite Imagery Road Extraction via Patch-Wise Keypoints Detection

• tile2net -> Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery

• sam_road -> Segment Anything Model (SAM) for large-scale, vectorized road network extraction from aerial imagery.

• LRDNet -> A Lightweight Road Detection Algorithm Based on Multiscale Convolutional Attention Network and Coupled Decoder Head

• Fine–Grained Extraction of Road Networks via Joint Learning of Connectivity and Segmentation -> uses SpaceNet 3 dataset

• Satellite-Image-Road-Segmentation -> Graph Reasoned Multi-Scale Road Segmentation in Remote Sensing Imagery

• PathFinder -> A Foundation Model for Road Mapping in Support of United Nations Humanitarian Affairs

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 -> Dataset creation

• SRBuildSeg -> Making low-resolution satellite images reborn: a deep learning approach for super-resolution building extraction

• 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.

• 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

• 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 -> 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

• 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

• 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

• SatFootprint -> building segmentation on the Spacenet 7 dataset

• Building-Detection -> Raster Vision experiment to train a model to detect buildings from satellite imagery in three cities in Latin America

• Multi-building-tracker -> Multi-target building tracker for satellite images using deep learning

• Boundary Enhancement Semantic Segmentation for Building Extraction

• Spacenet-Building-Detection

• LGPNet-BCD -> Building Change Detection for VHR Remote Sensing Images via Local-Global Pyramid Network and Cross-Task Transfer Learning Strategy

• MTL_homoscedastic_SRB -> A Multi-Task Deep Learning Framework for Building Footprint Segmentation

• FDANet -> Full-Level Domain Adaptation for Building Extraction in Very-High-Resolution Optical Remote-Sensing Images

• CBRNet -> A Coarse-to-fine Boundary Refinement Network for Building Extraction from Remote Sensing Imagery

• ASLNet -> Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images

• BRRNet -> A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images

• Multi-Scale-Filtering-Building-Index -> 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 -> Boundary Loss for Remote Sensing Imagery Semantic Segmentation

• Open Cities AI Challenge -> Segmenting Buildings for Disaster Resilience. Winning solutions on Github

• MAPNet -> Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery

• dual-hrnet -> localizing buildings and classifying their damage level

• ESFNet -> Efficient Network for Building Extraction from High-Resolution Aerial Images

• CVCMFFNet -> Complex-Valued Convolutional and Multifeature Fusion Network for Building Semantic Segmentation of InSAR Images

• STEB-UNet -> 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

• 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 -> Regularization of building boundaries in satellite images using adversarial and regularized losses

• PolyWorldPretrainedNetwork -> Polygonal Building Extraction with Graph Neural Networks in Satellite Images

• dl_image_segmentation -> 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

• UNetFormer -> A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery

• BES-Net -> Boundary Enhancing Semantic Context Network for High-Resolution Image Semantic Segmentation. Applied to Vaihingen and Potsdam datasets

• CVNet -> Contour Vibration Network for Building Extraction

• CFENet -> A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery

• HiSup -> Accurate Polygonal Mapping of Buildings in Satellite Imagery

• BuildingExtraction -> Building Extraction from Remote Sensing Images with Sparse Token Transformers

• CrossGeoNet -> A Framework for Building Footprint Generation of Label-Scarce Geographical Regions

• AFM_building -> Building Footprint Generation Through Convolutional Neural Networks With Attraction Field Representation

• RAMP (Replicable AI for MicroPlanning) -> building detection in low and middle income countries

• Building-instance-segmentation -> Multi-Modal Feature Fusion Network with Adaptive Center Point Detector for Building Instance Extraction

• CGSANet -> A Contour-Guided and Local Structure-Aware Encoder–Decoder Network for Accurate Building Extraction From Very High-Resolution Remote Sensing Imagery

• building-footprints-update -> Learning Color Distributions from Bitemporal Remote Sensing Images to Update Existing Building Footprints

• RAMP -> model and buildings dataset to support a wide variety of humanitarian use cases

• Thesis_Semantic_Image_Segmentation_on_Satellite_Imagery_using_UNets -> This master thesis aims to perform semantic segmentation of buildings on satellite images from the SpaceNet challenge 1 dataset using the U-Net architecture

• HD-Net -> High-resolution decoupled network for building footprint extraction via deeply supervised body and boundary decomposition

• RoofSense -> A novel deep learning solution for the automatic roofing material classification of the Dutch building stock using aerial imagery and laser scanning data fusion

• IBS-AQSNet -> Enhanced Automated Quality Assessment Network for Interactive Building Segmentation in High-Resolution Remote Sensing Imagery

• DeepMAO -> Deep Multi-scale Aware Overcomplete Network for Building Segmentation in Satellite Imagery

• CMGFNet-Building_Extraction -> Deep Learning Code for Building Extraction from very high resolution (VHR) remote sensing images

• Global Collinearity-aware Polygonizer for Polygonal Building Mapping in Remote Sensing

• Building Segmentation on LR-HR-SR Satellite Imagery -> perform building delineation on different types of satellite imagery: Low-Resolution (LR), High-Resolution (HR), and Super-Resolution (SR). The goal is to compare the performance of segmentation models across these varying resolutions.

• UrbanGraphSAGE -> Graph Neural Network (GraphSAGE) for urban building footprint extraction from Sentinel-2 satellite imagery

• terratorch-building-segmentation -> Fine-tuning Geospatial Foundation Models (Prithvi, TerraMind) for building footprint segmentation from Sentinel-2 using TerraTorch — Algiers case study

• MRPolyBuild -> code for paper: Rethinking Resolution: Large-Scale Polygonal Building Detection Using Medium-Resolution (3-5m) Satellite Data

Segmentation - Solar panels

• Deep-Learning-for-Solar-Panel-Recognition -> using both object detection with Yolov5 and Unet segmentation

• DeepSolar -> A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States. Dataset on kaggle, actually used a CNN for classification and segmentation is obtained by applying a threshold to the activation map. Original code is tf1 but tf2/kers and a pytorch implementation are available. Also checkout [Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in .. Virginia]

• hyperion_solar_net -> trained classificaton & segmentation models on RGB imagery from Google Maps

• 3D-PV-Locator -> Large-scale detection of rooftop-mounted photovoltaic systems in 3D

• PV_Pipeline -> 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 -> 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

• 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_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

• photovoltaic-detection -> Detecting available rooftop area from satellite images to install photovoltaic panels

• Solar_UNet -> U-Net models delineating solar arrays in Sentinel-2 imagery

• SolarDetection-solafune -> Solar Panel Detection Using Sentinel-2 for the Solafune Competition

• A Comparative Evaluation of Deep Learning Techniques for Photovoltaic Panel Detection from Aerial Images

• UCSD_MLBootcamp_Capstone -> Automatic Detection of Photovoltaic Power Stations Using Satellite Imagery and Deep Learning (Sentinel 2)

Segmentation - Ships & vessels

• Universal-segmentation-baseline-Kaggle-Airbus-Ship-Detection -> Kaggle Airbus Ship Detection Challenge - bronze medal solution

• Airbus-Ship-Segmentation -> unet

• contrastive_SSL_ship_detection -> Contrastive self supervised learning for ship detection in Sentinel 2 images

• airbus-ship-detection -> using DeepLabV3+

• Unet with web-application applied to Airbus ships

Segmentation - Other manmade

• Aarsh2001/ML_Challenge_NRSC -> Electrical Substation detection

• electrical_substation_detection

• MCAN-OilSpillDetection -> Oil Spill Detection with A Multiscale Conditional Adversarial Network under Small Data Training

• mining-detector -> detection of artisanal gold mines in Sentinel-2 satellite imagery for Amazon Mining Watch. Also covers clandestine airstrips

• EG-UNet Deep Feature Enhancement Method for Land Cover With Irregular and Sparse Spatial Distribution Features: A Case Study on Open-Pit Mining

• plastics -> Detecting and Monitoring Plastic Waste Aggregations in Sentinel-2 Imagery

• MADOS -> Detecting Marine Pollutants and Sea Surface Features with Deep Learning in Sentinel-2 Imagery on the MADOS dataset

• SADMA -> Residual Attention UNet on MARIDA: Marine Debris Archive is a marine debris-oriented dataset on Sentinel-2 satellite images

• MAP-Mapper -> Marine Plastic Mapper is a tool for assessing marine macro-plastic density to identify plastic hotspots, underpinned by the MARIDA dataset.

• substation-seg -> segmenting substations in Sentinel 2 satellite imagery

• SAMSelect -> An Automated Spectral Index Search for Marine Debris using Segment-Anything (SAM)

• MambaMPD -> code for paper: MambaMPD: A Mamba-Driven Segmentation Framework for Marine Pollution Detection from Remote Sensing Imagery

Panoptic segmentation

• Things and stuff or how remote sensing could benefit from panoptic segmentation

• utae-paps -> PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation

• 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

Segmentation - Miscellaneous

• seg-eval -> SegEval is a Python library that provides tools for evaluating semantic segmentation models. Generate evaluation regions and to analyze segmentation results within them.

• 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

• 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 -> Deep Covariance Alignment for Domain Adaptive Remote Sensing Image Segmentation

• SCAttNet -> Semantic Segmentation Network with Spatial and Channel Attention Mechanism

• Efficient-Transformer -> Efficient Transformer for Remote Sensing Image Segmentation

• weakly_supervised -> Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery

• HRCNet-High-Resolution-Context-Extraction-Network -> 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

• Spectralformer -> Rethinking hyperspectral image classification with transformers

• Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels

• Semantic-Segmentation-with-Sparse-Labels

• SNDF -> Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation

• dynamic-rs-segmentation -> Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks

• segmentation_models.pytorch -> Segmentation models with pretrained backbones, has been used in multiple winning solutions to remote sensing competitions

• SSRN -> Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework

• SO-DNN -> Simplified object-based deep neural network for very high resolution remote sensing image classification

• SANet -> Scale-Aware Network for Semantic Segmentation of High-Resolution Aerial Images

• aerial-segmentation -> Learning Aerial Image Segmentation from Online Maps

• Detectron2 FPN + PointRend Model for amazing Satellite Image Segmentation -> 15% increase in accuracy when compared to the U-Net model

• HybridSN -> Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification

• TNNLS_2022_X-GPN -> Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification

• singleSceneSemSegTgrs2022 -> Unsupervised Single-Scene Semantic Segmentation for Earth Observation

• A-Fast-and-Compact-3-D-CNN-for-HSIC -> A Fast and Compact 3-D CNN for Hyperspectral Image Classification

• HSNRS -> Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery

• GiGCN -> Graph-in-Graph Convolutional Network for Hyperspectral Image Classification

• SSAN -> 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 -> A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images

• wheelRuts_semanticSegmentation -> 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 hyperspectral image (HSI) segmentation

• ST-UNet -> Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation

• EDFT -> Efficient Depth Fusion Transformer for Aerial Image Semantic Segmentation

• WiCoNet -> Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images

• CRGNet -> Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations

• SA-UNet -> Improved U-Net Remote Sensing Classification Algorithm Fusing Attention and Multiscale Features

• MANet -> Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images

• BANet -> Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

• MACU-Net -> MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

• DNAS -> Decoupling Neural Architecture Search for High-Resolution Remote Sensing Image Semantic Segmentation

• A2-FPN -> A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

• MAResU-Net -> Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images

• RSEN -> Robust Self-Ensembling Network for Hyperspectral Image Classification

• MSNet -> multispectral semantic segmentation network for remote sensing images

• Swin-Transformer-Semantic-Segmentation -> Satellite Image Semantic Segmentation

• UDA_for_RS -> Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer

• A-3D-CNN-AM-DSC-model-for-hyperspectral-image-classification -> Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification

• contrastive-distillation -> A Contrastive Distillation Approach for Incremental Semantic Segmentation in Aerial Images

• SegForestNet -> SegForestNet: Spatial-Partitioning-Based Aerial Image Segmentation

• MFVNet -> MFVNet: Deep Adaptive Fusion Network with Multiple Field-of-Views for Remote Sensing Image Semantic Segmentation

• Wildebeest-UNet -> detecting wildebeest and zebras in Serengeti-Mara ecosystem from very-high-resolution satellite imagery

• segment-anything-eo -> Earth observation tools for Meta AI Segment Anything (SAM - Segment Anything Model)

• HR-Image-classification_SDF2N -> A Shallow-to-Deep Feature Fusion Network for VHR Remote Sensing Image Classification

• sink-seg -> Automatic Segmentation of Sinkholes Using a Convolutional Neural Network

• Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and Recommendations

• EMRT -> Enhancing Multiscale Representations With Transformer for Remote Sensing Image Semantic Segmentation

• UDA_for_RS -> Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer

• CMTFNet -> CMTFNet: CNN and Multiscale Transformer Fusion Network for Remote Sensing Image Semantic Segmentation

• CM-UNet -> Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation

• Using Stable Diffusion to Improve Image Segmentation Models -> Augmenting Data with Stable Diffusion

• SSRS -> Semantic Segmentation for Remote Sensing, multiple networks implemented

• BIOSCANN -> BIOdiversity Segmentation and Classification with Artificial Neural Networks

• ResUNet-a -> a deep learning framework for semantic segmentation of remotely sensed data

• SSG2 -> A New Modelling Paradigm for Semantic Segmentation

• DBFNet -> Deep Bilateral Filtering Network for Point-Supervised Semantic Segmentation in Remote Sensing Images

• PGNet -> PGNet: Positioning Guidance Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Images paper

• ASD -> Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors.

• u-nets-implementation -> Semantic-Segmentation-with-U-Nets

• SDM -> Scale-aware Detailed Matching for Few-Shot Aerial Image Semantic Segmentation

• Transferability-Remote-Sensing -> On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data

• data-centric-satellite-segmentation -> Contains implementations of data-centric approaches for improving semantic segmentation on satellite imagery, from Microsoft

• HSLabeling -> Towards Efficient Labeling for Large-scale Remote Sensing Image Segmentation with Hybrid Sparse Labeling

• RemoteSAM -> Towards Segment Anything for Earth Observation (SAM)

• HieraRS -> A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer

• MultiFranceFences -> Large-scale fence detection using deep learning and multimodal aerial imagery

Instance segmentation

In instance segmentation, each individual 'instance' of a segmented area is given a unique label. For detection of very small objects this may be a good approach, but it can struggle separating 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

• Building-Detection-MaskRCNN -> Building detection from the SpaceNet dataset by using Mask RCNN

• Mask_RCNN-for-Caravans -> detect caravan footprints from OS imagery

• parking_bays_detectron2 -> Detecting parking bays with satellite imagery. Used Detectron2 and synthetic data with Unreal, superior performance to using Mask RCNN

• 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 -> 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

• RSPrompter -> Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model

• VMD-Mask-RCNN-pipeline -> Detecting and segmenting sand mining river vessels on the Vietnam Mekong Delta, using PlanetScope imagery and Mask R-CNN

• BRIGHT cvprw26 -> Mask R-CNN baseline for multimodal building damage instance segmentation on BRIGHT

Object detection

Image showing the suitability of rotated bounding boxes in remote sensing.

Object detection in remote sensing involves locating and surrounding objects of interest with bounding boxes. Due to the large size of remote sensing images and the fact that objects may only comprise a few pixels, object detection can be challenging in this context. The imbalance between the area of the objects to be detected and the background, combined with the potential for objects to be easily confused with random features in the background, further complicates the task. Object detection generally performs better on larger objects, but becomes increasingly difficult as the objects become smaller and more densely packed. The accuracy of object detection models can also degrade rapidly as image resolution decreases, which is why it is common to use high resolution imagery, such as 30cm RGB, for object detection in remote sensing. A unique characteristic of aerial images is that objects can be oriented in any direction. To effectively extract measurements of the length and width of an object, it can be crucial to use rotated bounding boxes that align with the orientation of the object. This approach enables more accurate and meaningful analysis of the objects within the image. Image source

Object tracking in videos

• TCTrack -> Temporal Contexts for Aerial Tracking

• CFME -> Object Tracking in Satellite Videos by Improved Correlation Filters With Motion Estimations

• TGraM -> 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 -> DSFNet: Dynamic and Static Fusion Network for Moving Object Detection in Satellite Videos

• HiFT -> Hierarchical Feature Transformer for Aerial Tracking

• geo-trax -> extracts georeferenced vehicle trajectories from high-altitude bird's-eye-view drone video

Object detection with rotated bounding boxes

Orinted bounding boxes (OBB) are polygons representing rotated rectangles. For datasets checkout DOTA & HRSC2016. Start with Yolov8

• mmrotate -> Rotated Object Detection Benchmark, with pretrained models and function for inferencing on very large images

• OrientedDet -> a lightweight PyTorch framework for rotated object detection in aerial and satellite imagery, with oriented models, geometry operations and DOTA support

• 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 -> Align Deep Features for Oriented Object Detection

• CFC-Net -> A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images

• 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 -> Arbitrary-Oriented Object Detection with Circular Smooth Label

• r3det-on-mmdetection -> R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

• R-DFPN_FPN_Tensorflow -> Rotation Dense Feature Pyramid Networks (Tensorflow)

• R2CNN_Faster-RCNN_Tensorflow -> Rotational region detection based on Faster-RCNN

• Rotated-RetinaNet -> implemented in pytorch, it supports the following datasets: DOTA, HRSC2016, ICDAR2013, ICDAR2015, UCAS-AOD, NWPU VHR-10, VOC2007

• OBBDet_Swin -> The sixth place winning solution in 2021 Gaofen Challenge

• CG-Net -> Learning Calibrated-Guidance for Object Detection in Aerial Images

• OrientedRepPoints_DOTA -> Oriented RepPoints + Swin Transformer/ReResNet

• yolov5_obb -> yolov5 + Oriented Object Detection

• How to Train YOLOv5 OBB -> YOLOv5 OBB tutorial and YOLOv5 OBB noteboook

• OHDet_Tensorflow -> can be applied to rotation detection and object heading detection

• Seodore -> framework maintaining recent updates of mmdetection

• Rotation-RetinaNet-PyTorch -> oriented detector Rotation-RetinaNet implementation on Optical and SAR ship dataset

• AIDet -> an open source object detection in aerial image toolbox based on MMDetection

• rotation-yolov5 -> rotation detection based on yolov5

• SLRDet -> project based on mmdetection to reimplement RRPN and use the model Faster R-CNN OBB

• AxisLearning -> Axis Learning for Orientated Objects Detection in Aerial Images

• Detection_and_Recognition_in_Remote_Sensing_Image -> This work uses PaNet to realize Detection and Recognition in Remote Sensing Image by MXNet

• DrBox-v2-tensorflow -> tensorflow implementation of DrBox-v2 which is an improved detector with rotatable boxes for target detection in remote sensing images

• Rotation-EfficientDet-D0 -> A PyTorch Implementation Rotation Detector based EfficientDet Detector, applied to custom rotation vehicle datasets

• DODet -> Dual alignment for oriented object detection, uses DOTA dataset

• GF-CSL -> Gaussian Focal Loss: Learning Distribution Polarized Angle Prediction for Rotated Object Detection in Aerial Images

• Polar-Encodings -> 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 -> A One-Stage Anchor-Free Approach for Oriented Object Detection

• 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 -> A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection

• NPMMR-Det -> A Novel Nonlocal-Aware Pyramid and Multiscale Multitask Refinement Detector for Object Detection in Remote Sensing Images

• AOPG -> Anchor-Free Oriented Proposal Generator for Object Detection

• SE2-Det -> Semantic-Edge-Supervised Single-Stage Detector for Oriented Object Detection in Remote Sensing Imagery

• OrientedRepPoints -> Oriented RepPoints for Aerial Object Detection

• TS-Conv -> Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images

• FCOSR -> A Simple Anchor-free Rotated Detector for Aerial Object Detection. This implement is modified from mmdetection. See also TensorRT_Inference

• OBB_Detection -> Finalist's solution in the track of Oriented Object Detection in Remote Sensing Images, 2022 Guangdong-Hong Kong-Macao Greater Bay Area International Algorithm Competition

• sam-mmrotate -> SAM (Segment Anything Model) for generating rotated bounding boxes with MMRotate, which is a comparison method of H2RBox-v2

• mmrotate-dcfl -> Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection

• h2rbox-mmrotate -> Horizontal Box Annotation is All You Need for Oriented Object Detection

• Spatial-Transform-Decoupling -> Spatial Transform Decoupling for Oriented Object Detection

• ARS-DETR -> Aspect Ratio Sensitive Oriented Object Detection with Transformer

• CFINet -> Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning. Introduces SODA-A dataset

• FRCNN_git -> Faster R-CNN implementation for rotated boxes

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 -> Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network. Applied to COWC & OGST datasets

• FBNet -> Feature Balance for Fine-Grained Object Classification in Aerial Images

• SuperYOLO -> 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 -> Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images

• MCCNet -> Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images

• CorrNet -> Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation

• Reading list for deep learning based Salient Object Detection in Optical Remote Sensing Images

• ORSSD-dataset -> salient object detection dataset

• EORSSD-dataset -> Extended Optical Remote Sensing Saliency Detection (EORSSD) Dataset

• DAFNet_TIP20 -> Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images

• EMFINet -> Edge-Aware Multiscale Feature Integration Network for Salient Object Detection in Optical Remote Sensing Images

• ERPNet -> Edge-guided Recurrent Positioning Network for Salient Object Detection in Optical Remote Sensing Images

• FSMINet -> Fully Squeezed Multi-Scale Inference Network for Fast and Accurate Saliency Detection in Optical Remote Sensing Images

• AGNet -> AGNet: Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images

• MSCNet -> A lightweight multi-scale context network for salient object detection in optical remote sensing images

• GPnet -> Global Perception Network for Salient Object Detection in Remote Sensing Images

• SeaNet -> Lightweight Salient Object Detection in Optical Remote Sensing Images via Semantic Matching and Edge Alignment

• GeleNet -> Salient Object Detection in Optical Remote Sensing Images Driven by Transformer

Object detection - Buildings, rooftops & solar panels

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

• Building Extraction with YOLT2 and SpaceNet Data

• XBD-hurricanes -> Models for building (and building damage) detection in high-resolution (<1m) satellite and aerial imagery using a modified RetinaNet model

• 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

• 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 -> 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, vessels & wake

• Airbus Ship Detection Challenge -> using oriented bounding boxes. Read Detecting ships in satellite imagery: five years later…

• kaggle-ships-in-Google-Earth-yolov8 -> Applying YOLOv8 to Kaggle Ships in Google Earth dataset

• How hard is it for an AI to detect ships on satellite images?

• SARfish -> Ship detection in Sentinel 1 Synthetic Aperture Radar (SAR) imagery

• Arbitrary-Oriented Ship Detection through Center-Head Point Extraction

• ship_detection -> using an interesting combination of CNN classifier, Class Activation Mapping (CAM) & UNET segmentation

• 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

• shipsnet-detector -> Detect container ships in Planet imagery using machine learning

• 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

• vessel-detection-sentinels -> Sentinel-1 and Sentinel-2 Vessel Detection

• 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 -> Ship Detection in Optical Remote Sensing Images Based on Gaussian Heatmap Regression

• LR-TSDet -> 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

• WakeNet -> Rethinking Automatic Ship Wake Detection: State-of-the-Art CNN-based Wake Detection via Optical Images

• LEVIR-Ship -> a dataset for tiny ship detection under medium-resolution remote sensing images

• Push-and-Pull-Network -> Contrastive Learning for Fine-grained Ship Classification in Remote Sensing Images

• DRENet -> 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.

• vessel-detection-viirs -> Model and service code for streaming vessel detections from VIIRS satellite imagery

• wakemodel_llmassist -> wake detection in Sentinel-2, uses an EfficientNet-B0 architecture adapted for keypoint detection

• ORFENet -> Tiny Object Detection in Remote Sensing Images Based on Object Reconstruction and Multiple Receptive Field Adaptive Feature Enhancement. Uses LEVIR-Ship & AI-TODv2 datasets

• mayrajeo S2 ship-detection -> Detecting marine vessels from Sentinel-2 imagery with YOLOv8

• CHPDet -> PyTorch implementation of "Arbitrary-Oriented Ship Detection through Center-Head Point Extraction"

• VDS2Raw -> VFNet with ResNet-18 for Vessel Detection in S-2 Raw Imagery

• Global Fishing Capacity - Vessel Detection Model -> from Allen.ai and using Maxar 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. Not sctictly object detection but a CNN to predict the car count in a tile

• CarCounting -> using Yolov3 & COWC dataset

• 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

• Vehicle-Counting-in-Very-Low-Resolution-Aerial-Images -> Vehicle Counting in Very Low-Resolution Aerial Images via Cross-Resolution Spatial Consistency and Intraresolution Time Continuity

• detecting-trucks -> detecting large vehicles in Sentinel-2

• geo-trax -> detects and tracks cars, buses, trucks & motorcycles in high-altitude drone video, output as georeferenced trajectories

Object detection - Planes & aircraft

• FlightScope_Bench -> A Deep Comprehensive Assessment of Aircraft Detection Algorithms in Satellite Imagery, including Faster RCNN, DETR, SSD, RTMdet, RetinaNet, CenterNet, YOLOv5, and YOLOv8

• 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

• 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

• 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

• rareplanes-yolov5 -> using YOLOv5 and the RarePlanes dataset to detect and classify sub-characteristics of aircraft, with article

• OnlyPlanes -> Incrementally Tuning Synthetic Training Datasets for Satellite Object Detection

• Efficient-YOLO-RS-Airplane-Detection - Implementation of YOLOv8 and YOLOv9 for efficient airplane detection in VHR satellite imagery (2025).

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

• WindTurbineDetection -> Implementation of transfer learning approach using the YOLOv7 framework to detect and rapidly quantify wind turbines in raw LANDSAT and NAIP satellite imagery

• Arctic-Infrastructure-Detection-Paper -> Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery paper

Object detection - Oil storage tank detection

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-Tank-Volume-Estimation -> combines object detection and classical computer vision

• SubpixelCircleDetection -> CIRCULAR-SHAPED OBJECT DETECTION IN LOW RESOLUTION SATELLITE IMAGES

• oil_well_detector -> detect oil wells in the Bakken oil field based on satellite imagery

• AContrarioTankDetection -> Oil Tank Detection in Satellite Images via a Contrario Clustering

• Fast-Large-Image-Object-Detection-yolov7 -> The oil yolov7 model is trained on oil storage tanks (OST) dataset

• Oiltank-Capacity-Detection -> Analyse storage tanks around the world and identify the external floating roof tanks.

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)

• deepCattleCount -> CSRNet-based cattle counting in very high-resolution satellite imagery to study land use and policy impacts in the Brazilian Amazon

• 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 -> Deep object detection for waterbird monitoring using aerial imagery

• Beluga Whale Detection from Satellite Imagery with Point Labels

• HerdNet -> From Crowd to Herd Counting: How to Precisely Detect and Count African Mammals using Aerial Imagery and Deep Learning?

• sat-rhino -> evaluating a YOLOv12 model, plus tools for generating synthetic data in Blender

Object detection - Miscellaneous

• 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

• 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

• Tensorflow Benchmarks for Object Detection in Aerial Images

• Pytorch Benchmarks for Object Detection in Aerial Images

• ASPDNet -> Counting dense objects in remote sensing images

• xview-yolov3 -> xView 2018 Object Detection Challenge: YOLOv3 Training and Inference

• Faster RCNN for xView satellite data challenge

• 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

• Electric-Pylon-Detection-in-RSI -> a dataset which contains 1500 remote sensing images of electric pylons used to train ten deep learning models

• IS-Count -> IS-Count is a sampling-based and learnable method for estimating the total object count in a region

• 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-cored 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 dataset

• Google-earth-Object-Recognition -> Code for training and evaluating on Dior Dataset (Google Earth Images) using RetinaNet and YOLOV5

• Detection of Multiclass Objects in Optical Remote Sensing Images -> Detection of Multiclass Objects in Optical Remote Sensing Images

• SB-MSN -> 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

• OPLD-Pytorch -> Learning Point-Guided Localization for Detection in Remote Sensing Images

• F3Net -> Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images

• GLNet -> Global to Local: Clip-LSTM-Based Object Detection From Remote Sensing Images

• SRAF-Net -> A Scene-Relevant Anchor-Free Object Detection Network in Remote Sensing Images

• SHAPObjectDetection -> SHAP-Based Interpretable Object Detection Method for Satellite Imagery

• NWD -> A Normalized Gaussian Wasserstein Distance for Tiny Object Detection. Uses AI-TOD dataset

• MSFC-Net -> Multiscale Semantic Fusion-Guided Fractal Convolutional Object Detection Network for Optical Remote Sensing Imagery

• LO-Det -> LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images

• R2IPoints -> Pursuing Rotation-Insensitive Point Representation for Aerial Object Detection

• Object-Detection -> Multi-Scale Object Detection with the Pixel Attention Mechanism in a Complex Background

• mmdet-rfla -> RFLA: Gaussian Receptive based Label Assignment for Tiny Object Detection

• Interactive-Multi-Class-Tiny-Object-Detection -> Interactive Multi-Class Tiny-Object Detection

• small-object-detection-benchmark -> Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection (SAHI)

• OD-Satellite-iSAID -> Object Detection in Aerial Images: A Case Study on Performance Improvement using iSAID

• Large-Selective-Kernel-Network -> Large Selective Kernel Network for Remote Sensing Object Detection

• Satellite_Imagery_Detection_YOLOV7 -> YOLOV7 applied to xView1 Dataset

• FSANet -> FSANet: Feature-and-Spatial-Aligned Network for Tiny Object Detection in Remote Sensing Images

• OAN Fewer is More: Efficient Object Detection in Large Aerial Images, based on MMdetection

• DOTA-C -> evaluating the robustness of object detection models to 19 types of image quality degradation

• Satellite-Remote-Sensing-Image-Object-Detection -> using RefineDet & DOTA dataset

• SFRNet -> SFRNet: Fine-Grained Oriented Object Recognition via Separate Feature Refinement

• contrail-seg -> Neural network models for contrail detection and segmentation

• DQ-DETR -> DETR with Dynamic Query for Tiny Object Detection, uses AI-TOD-v1 and AI-TOD-v2 Datasets

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

• cownter_strike -> counting cows, located with point-annotations, two models: CSRNet (a density-based method) & LCFCN (a detection-based method)

• Bayesian-Car-Counting -> car counting in overhead imagery using Bayesian loss with point supervision on the COWC dataset

• TreeMatch -> tree density estimation from satellite imagery using optimal transport and mixed strong and weak point supervision; includes the multi-sensor TinyTrees benchmark

• 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

• Counting from Sky -> A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method

• PSGCNet -> PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images

• psgcnet -> A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote-Sensing Images

Regression

Regression prediction of windspeed.

Regression in remote sensing involves predicting continuous variables such as wind speed, tree height, or soil moisture from an image. Both classical machine learning and deep learning approaches can be used to accomplish this task. Classical machine learning utilizes feature engineering to extract numerical values from the input data, which are then used as input for a regression algorithm like linear regression. On the other hand, deep learning typically employs a convolutional neural network (CNN) to process the image data, followed by a fully connected neural network (FCNN) for regression. The FCNN is trained to map the input image to the desired output, providing predictions for the continuous variables of interest. Image source

• GEDI-BDL -> Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles

• Global-Canopy-Height-Map -> Estimating Canopy Height at Scale (ICML2024)

• HighResCanopyHeight -> code for Meta paper: Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar

• OpticalWaveGauging_DNN -> Optical wave gauging using deep neural networks

• satellite-pose-estimation -> adapts a ResNet50 model architecture to perform pose estimation on several series of satellite images (both real and synthetic)

• Tropical Cyclone Wind Estimation Competition -> on RadiantEarth MLHub

• DengueNet -> DengueNet: Dengue Prediction using Spatiotemporal Satellite Imagery for Resource-Limited Countries

• tropical_cyclone_uq -> Uncertainty Aware Tropical Cyclone Wind Speed Estimation from Satellite Data

• AQNet -> Predicting air quality via multimodal AI and satellite imagery

• Soil Moisture Retrieval from Google Research -> A Deep Learning Data Fusion Model Using Sentinel-1/2, SoilGrids, SMAP, and GLDAS for Soil Moisture Retrieval

• temp-mosaiks -> predict ground-level temperature with MOSAIKS

• biomsharp -> Biomass Super-resolution for High AccuRacy Prediction

• TREASURE-NET -> Super-Resolved Canopy Height Mapping from Sentinel-2 Time Series Using LiDAR HD Reference Data across Metropolitan France

• Seabed-Net -> A multi-task network for joint bathymetry and pixel-based seabed classification from remote sensing imagery in shallow waters, uses MagicBathyNet dataset

• ECHOSAT -> Estimating Canopy Height Over Space And Time, uses a Swin Video UNet architecture that processes multi-sensor satellite data.

• Popcorn -> High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2, with follow up work Bourbon

• UrbanControlNet -> Envisioning Global Urban Development with Satellite Imagery and Generative AI.

• Emb2Heights -> baseline for the Emb2Heights challenge - trains and runs inference for a model that predicts sub-pixel land cover percentages (Building, Vegetation, Water) and continuous structure heights (nDSM) directly from Earth Observation embeddings

Cloud detection & removal

(left) False colour image and (right) a cloud & shadow mask.

Clouds are a major issue in remote sensing images as they can obscure the underlying ground features. This hinders the accuracy and effectiveness of remote sensing analysis, as the obscured regions cannot be properly interpreted. In order to address this challenge, various techniques have been developed to detect clouds in remote sensing images. Both classical algorithms and deep learning approaches can be employed for cloud detection. Classical algorithms typically use threshold-based techniques and hand-crafted features to identify cloud pixels. However, these techniques can be limited in their accuracy and are sensitive to changes in image appearance and cloud structure. On the other hand, deep learning approaches leverage the power of convolutional neural networks (CNNs) to accurately detect clouds in remote sensing images. These models are trained on large datasets of remote sensing images, allowing them to learn and generalize the unique features and patterns of clouds. The generated cloud mask can be used to identify the cloud pixels and eliminate them from further analysis or, alternatively, cloud inpainting techniques can be used to fill in the gaps left by the clouds. This approach helps to improve the accuracy of remote sensing analysis and provides a clearer view of the ground, even in the presence of clouds. Image adapted from the paper 'Refined UNet Lite: End-to-End Lightweight Network for Edge-precise Cloud Detection'

• CloudSEN12 -> Sentinel 2 cloud dataset with a varierty of models here

• 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

• 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 -> 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 -> 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 -> STGAN for Cloud Removal in Satellite Images

• mcgan-cvprw2017-pytorch -> 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 -> 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

• 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 -> 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 -> Generative Adversarial Training for Weakly Supervised Cloud Matting

• km_predict -> KappaMask, or km-predict, is a cloud detector for Sentinel-2 Level-1C and Level-2A input products applied to S2 full image prediction

• CDnet -> CNN-Based Cloud Detection for Remote Sensing Imager

• GLNET -> Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments

• CDnetV2 -> CNN-Based Cloud Detection for Remote Sensing Imagery With Cloud-Snow Coexistence

• grouped-features-alignment -> 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 -> 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

• 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 -> CloudX-net: A robust encoder-decoder architecture for cloud detection from satellite remote sensing images

• 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

• cloud-detection-venus -> Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types

• explaining_cloud_effects -> Explaining the Effects of Clouds on Remote Sensing Scene Classification

• Clouds-Images-Segmentation -> Marine Stratocumulus Cloud-Type Classification from SEVIRI Using Convolutional Neural Networks

• DeCloud-GAN -> DeCloud GAN: An Advanced Generative Adversarial Network for Removing Cloud Cover in Optical Remote Sensing Imagery

• cloud_segmentation_comparative -> BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery

• PLFM-Clouds-Removal -> Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model

• Cloud-removal-model-collection -> A collection of the existing end-to-end cloud removal models

• SEnSeIv2 -> Sensor Independent Cloud and Shadow Masking with Ambiguous Labels and Multimodal Inputs

• cloud-detection-venus -> Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types

• UnCRtainTS -> Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series

• U-TILISE -> A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series

• cloudtran -> Cloud removal from multi-temporal satellite images using axial transformer networks

• self-supervised-cloud-detection -> Self-supervised representation learning for cloud detection using Sentinel-2 images

• AGFlow-model -> A timestamp-conditioned spatiotemporal flow-matching framework for asynchronous Sentinel-1 SAR / Sentinel-2 optical fusion, targeting cloud removal, missing-frame reconstruction, and anytime optical image generation

Change detection

(left) Initial and (middle) after some development, with (right) the change highlighted.

Change detection is a vital component of remote sensing analysis, enabling the monitoring of landscape changes over time. This technique can be applied to identify a wide range of changes, including land use changes, urban development, coastal erosion, and deforestation. Change detection can be performed on a pair of images taken at different times, or by analyzing multiple images collected over a period of time. It is important to note that while change detection is primarily used to detect changes in the landscape, it can also be influenced by the presence of clouds and shadows. These dynamic elements can alter the appearance of the image, leading to false positives in change detection results. Therefore, it is essential to consider the impact of clouds and shadows on change detection analysis, and to employ appropriate methods to mitigate their influence. Image source

• 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

• STANet ->STANet for remote sensing image change detection

• 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

• BIT_CD -> Official Pytorch Implementation of Remote Sensing Image Change Detection with Transformers

• Unstructured-change-detection-using-CNN

• QGIS plugin for applying change detection algorithms on high resolution satellite imagery

• Fully Convolutional Siamese Networks for Change Detection

• Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks -> used the Onera Satellite Change Detection (OSCD) dataset

• 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 -> 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

• planet-movement -> Find and process Planet image pairs to highlight object movement

• 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 -> A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection

• SRCDNet -> 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

• A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images

• 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

• 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 -> Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images

• SiROC -> 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 -> 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 -> FCCDN: Feature Constraint Network for VHR Image Change Detection.

• INLPG_Python -> Structure Consistency based Graph for Unsupervised Change Detection with Homogeneous and Heterogeneous Remote Sensing Images

• NSPG_Python -> Nonlocal patch similarity based heterogeneous remote sensing change detection

• LGPNet-BCD -> Building Change Detection for VHR Remote Sensing Images via Local-Global Pyramid Network and Cross-Task Transfer Learning Strategy

• DS_UNet -> Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection using a Dual Stream U-Net, uses Onera Satellite Change Detection dataset

• SiameseSSL -> 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 -> Fusing Multi-modal Data for Supervised Change Detection

• Unsupervised-CD-in-SITS-using-DL-and-Graphs -> Unsupervised Change Detection Analysis in Satellite Image Time Series using Deep Learning Combined with Graph-Based Approaches

• LSNet -> Extremely Light-Weight Siamese Network For Change Detection in Remote Sensing Image

• End-to-end-CD-for-VHR-satellite-image -> End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

• Semantic-Change-Detection -> SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery

• ERCNN-DRS_urban_change_monitoring -> Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

• EGRCNN -> Edge-guided Recurrent Convolutional Neural Network for Multi-temporal Remote Sensing Image Building Change Detection

• Unsupervised-Remote-Sensing-Change-Detection -> An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning

• CropLand-CD -> A CNN-transformer Network with Multi-scale Context Aggregation for Fine-grained Cropland Change Detection

• contrastive-surface-image-pretraining -> Supervising Remote Sensing Change Detection Models with 3D Surface Semantics

• dcvaVHROptical -> Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images

• hyperdimensionalCD -> Change Detection in Hyperdimensional Images Using Untrained Models

• DSFANet -> 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 -> 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 -> An End-to-End Superpixel-Enhanced Change Detection Network for Very-High-Resolution Remote Sensing Images

• deforestation-detection -> 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

• SentinelClearcutDetection -> Scripts for deforestation detection on the Sentinel-2 Level-A images

• clearcut_detection -> research & web-service for clearcut detection

• CDRL -> Unsupervised Change Detection Based on Image Reconstruction Loss

• ddpm-cd -> Remote Sensing Change Detection (Segmentation) using Denoising Diffusion Probabilistic Models

• DreamCD -> Change detection in remote sensing images

• Remote-sensing-time-series-change-detection -> Graph-based block-level urban change detection using Sentinel-2 time series

• dfc2021-msd-baseline -> Multitemporal Semantic Change Detection track of the 2021 IEEE GRSS Data Fusion Competition

• CorrFusionNet -> Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion

• IRCNN -> 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 -> TINYCD: A (Not So) Deep Learning Model For Change Detection. Uses LEVIR-CD & WHU-CD datasets

• FHD -> Feature Hierarchical Differentiation for Remote Sensing Image Change Detection

• building-expansion -> Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms

• SaDL_CD -> Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection

• EGCTNet_pytorch -> Building Change Detection Based on an Edge-Guided Convolutional Neural Network Combined with a Transformer

• S2-cGAN -> S2-cGAN: Self-Supervised Adversarial Representation Learning for Binary Change Detection in Multispectral Images

• A-loss-function-for-change-detection -> UAL: Unchanged Area Loss-Function for Change Detection Networks

• IEEE_TGRS_SSTFormer -> Spectral–Spatial–Temporal Transformers for Hyperspectral Image Change Detection

• DMINet -> Change Detection on Remote Sensing Images Using Dual-Branch Multilevel Intertemporal Network

• AFCF3D-Net -> Adjacent-level Feature Cross-Fusion with 3D CNN for Remote Sensing Image Change Detection

• DSAHRNet -> A Deeply Attentive High-Resolution Network for Change Detection in Remote Sensing Images

• RDPNet -> RDP-Net: Region Detail Preserving Network for Change Detection

• BGAAE_CD -> Bipartite Graph Attention Autoencoders for Unsupervised Change Detection Using VHR Remote Sensing Images

• Metric-CD -> Deep Metric Learning for Unsupervised Change Detection in Remote Sensing Images

• HANet-CD -> HANet: A hierarchical attention network for change detection with bi-temporal very-high-resolution remote sensing images

• SRGCAE -> Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning

• change_detection_onera_baselines -> Siamese version of U-Net baseline model

• SiamCRNN -> Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network

• Graph-based methods for change detection in remote sensing images -> Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection

• TransUNetplus2 -> TransU-Net++: Rethinking attention gated TransU-Net for deforestation mapping. Uses the Amazon and Atlantic forest dataset

• AR-CDNet -> Towards Accurate and Reliable Change Detection of Remote Sensing Images via Knowledge Review and Online Uncertainty Estimation

• CICNet -> Compact Intertemporal Coupling Network for Remote Sensing Change Detection

• BGINet -> Remote Sensing Image Change Detection with Graph Interaction

• DSNUNet -> DSNUNet: An Improved Forest Change Detection Network by Combining Sentinel-1 and Sentinel-2 Images

• Forest-CD -> Forest-CD: Forest Change Detection Network Based on VHR Images

• S3Net_CD -> Superpixel-Guided Self-Supervised Learning Network for Change Detection in Multitemporal Image Change Detection

• T-UNet -> T-UNet: Triplet UNet for Change Detection in High-Resolution Remote Sensing Images

• UCDFormer -> UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation

• satellite-change-events -> Change Event Dataset for Discovery from Spatio-temporal Remote Sensing Imagery, uses Sentinel 2 CaiRoad & CalFire datasets

• CACo -> Change-Aware Sampling and Contrastive Learning for Satellite Images

• LightCDNet -> LightCDNet: Lightweight Change Detection Network Based on VHR Images

• OpenMineChangeDetection -> Characterising Open Cast Mining from Satellite Data (Sentinel 2), implements TinyCD, LSNet & DDPM-CD

• multi-task-L-UNet -> A Deep Multi-Task Learning Framework Coupling Semantic Segmentation and Fully Convolutional LSTM Networks for Urban Change Detection. Applied to SpaceNet7 dataset

• urban_change_detection -> Detecting Urban Changes With Recurrent Neural Networks From Multitemporal Sentinel-2 Data. fabric is another implementation

• UNetLSTM -> Detecting Urban Changes With Recurrent Neural Networks From Multitemporal Sentinel-2 Data

• SDACD -> An End-to-end Supervised Domain Adaptation Framework for Cross-domain Change Detection

• CycleGAN-Based-DA-for-CD -> CycleGAN-based Domain Adaptation for Deforestation Detection

• CGNet-CD -> Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery

• PA-Former -> PA-Former: Learning Prior-Aware Transformer for Remote Sensing Building Change Detection

• AERNet -> AERNet: An Attention-Guided Edge Refinement Network and a Dataset for Remote Sensing Building Change Detection (HRCUS-CD)

• S1GFlood-Detection -> DAM-Net: Global Flood Detection from SAR Imagery Using Differential Attention Metric-Based Vision Transformers. Includes S1GFloods dataset

• Changen -> Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process

• TTP -> Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for Remote Sensing Image Change Detection

• SAM-CD -> Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images

• SCanNet -> Joint Spatio-Temporal Modeling for Semantic Change Detection in Remote Sensing Images

• ELGC-Net -> Efficient Local-Global Context Aggregation for Remote Sensing Change Detection

• Official_Remote_Sensing_Mamba -> RS-Mamba for Large Remote Sensing Image Dense Prediction

• ChangeMamba -> Remote Sensing Change Detection with Spatio-Temporal State Space Model

• ClearSCD -> Comprehensively leveraging semantics and change relationships for semantic change detection in high spatial resolution remote sensing imagery

• RSCaMa -> Remote Sensing Image Change Captioning with State Space Model

• ChangeBind -> A Hybrid Change Encoder for Remote Sensing Change Detection

• OctaveNet -> An efficient multi-scale pseudo-siamese network for change detection in remote sensing images

• MaskCD -> A Remote Sensing Change Detection Network Based on Mask Classification

• I3PE -> Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange

• BDANet -> Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images

• BAN -> A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detection

• ubdd -> Learning Efficient Unsupervised Satellite Image-based Building Damage Detection, uses xView2

• SGSLN -> Exchanging Dual-Encoder–Decoder: A New Strategy for Change Detection With Semantic Guidance and Spatial Localization

• ChangeViT -> Unleashing Plain Vision Transformers for Change Detection

• pytorch-change-models -> out-of-box contemporary spatiotemporal change model implementations, standard metrics, and datasets

• FFCTL -> A full-level fused cross-task transfer learning method for building change detection using noise-robust pretrained networks on crowdsourced labels

• SARAS-Net -> SARAS-Net: Scale And Relation Aware Siamese Network for Change Detection

• Change_Detection_FCNs -> Deforestation Detection with Fully Convolutional Networks in the Amazon Forest from Landsat-8 and Sentinel-2 Images

• HyperNet -> HyperNet: Self-Supervised Hyperspectral SpatialSpectral Feature Understanding Network for Hyperspectral Change Detection

• CMCDNet -> CMCDNet: Cross-modal change detection flood extraction based on convolutional neural network

• Dsfer-Net -> A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Network

• Simple-Remote-Sensing-Change-Detection-Framework -> Simplified implementation of remote sensing change detection based on Pytorch

• BCE-Net -> BCE-Net: Reliable Building Footprints Change Extraction based on Historical Map and Up-to-Date Images using Contrastive Learning

• sits-change-detection -> Detecting Land Cover Changes Between Satellite Image Time Series By Exploiting Self-Supervised Representation Learning Capabilities

• USSFC-Net -> Ultralightweight Spatial–Spectral Feature Cooperation Network for Change Detection in Remote Sensing Images

• VcT_Remote_Sensing_Change_Detection -> VcT: Visual change Transformer for Remote Sensing Image Change Detection

• HabitAlp 2.0 -> Habitat and Land Cover Change Detection in Alpine Protected Areas: A Comparison of AI Architectures

• ChangeDINO -> DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery.

• mason_cd -> Make Some Noise: Unsupervised Remote Sensing Change Detection Using Latent Space Perturbations

• Noise2Map -> End-to-End Diffusion Model for Semantic Segmentation and Change Detection

• MBCTD -> Multi-Label Building Change Type Detection

• FAIR-EO-CD-benchmark -> code for paper: A comprehensive and trustworthy benchmark of AI methods for change detection in Earth observation

• CF-MAE -> code for paper: CF-MAE: A Change-Fused Masked Autoencoder for Earth Observation-Based Multi-Hazard Change Detection

Time series

Prediction of the next image in a series.

The analysis of time series observations in remote sensing data has numerous applications, including enhancing the accuracy of classification models and forecasting future patterns and events. Image source. Note: since classifying crops and predicting crop yield are such prominent use case for time series data, these tasks have dedicated sections after this one.

• 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 -> 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

• lightweight-temporal-attention-pytorch -> Light Temporal Attention Encoder (L-TAE) for satellite image time series

• dtwSat -> Time-Weighted Dynamic Time Warping for satellite image time series analysis

• MTLCC -> Multitemporal Land Cover Classification Network. A recurrent neural network approach to encode multi-temporal data for land cover classification

• PWWB -> Real-Time Spatiotemporal Air Pollution Prediction with Deep Convolutional LSTM through Satellite Image Analysis

• spaceweather -> predicting geomagnetic storms from satellite measurements of the solar wind and solar corona, uses LSTMs

• ConvTimeLSTM -> Extension of ConvLSTM and Time-LSTM for irregularly spaced images, appropriate for Remote Sensing

• ConvLSTM -> a PyTorch implementation of convolutional LSTM networks for precipitation nowcasting

• dl-time-series -> Deep Learning algorithms applied to characterization of Remote Sensing time-series

• tpe -> Generalized Classification of Satellite Image Time Series With Thermal Positional Encoding

• wildfire_forecasting -> 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

• Deep Learning for Cloud Gap-Filling on Normalized Difference Vegetation Index using Sentinel Time-Series -> A CNN-RNN based model that identifies correlations between optical and SAR data and exports dense Normalized Difference Vegetation Index (NDVI) time-series of a static 6-day time resolution and can be used for Events Detection tasks

• DeepSatModels -> ViTs for SITS: Vision Transformers for Satellite Image Time Series

• Presto -> Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

• LULC mapping using time series data & spectral bands -> uses 1D convolutions that learn from time-series data. Accompanies blog post: Time-Traveling Pixels: A Journey into Land Use Modeling

• hurricane-net -> A deep learning framework for forecasting Atlantic hurricane trajectory and intensity.

• CAPES -> Construction changes are detected using the U-net model and satellite time series

• Exchanger4SITS -> Rethinking the Encoding of Satellite Image Time Series

• Rapid Wildfire Hotspot Detection Using Self-Supervised Learning on Temporal Remote Sensing Data

• stenn-pytorch -> A Spatio-temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series

• RQUNet-DPC -> Dense Predictive Coding and UNet framework for satellite image time series segmentation

• encroaching-species-cerrado -> Detecting Encroaching Species in the Cerrado Using Deep Learning Time-Series Classification

• SITS-Former -> SITS-Former: A Pre-Trained Spatio-Spectral-Temporal Representation Model for Sentinel-2 Time Series Classification

• graph-dynamic-earth-net -> Graph Dynamic Earth Net: Spatio-Temporal Graph Benchmark for Satellite Image Time Series paper

• multi-stage-convSTAR-network -> Pytorch implementation for hierarchical time series classification with multi-stage convolutional RNN

• RESTORE-DiT -> Reliable satellite image time series reconstruction by multimodal sequential diffusion transformer

• CanadaFireSat -> CNN-based using ResNet encoders and Transformer-based using ViT encoders

• MMNet -> Integration of Snapshot and Time Series Data for Improving SMAP Soil Moisture Downscaling

• CNN-LSTM_for_DSM -> A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based phenological variables

• S-TSViT -> Spiking Temporo-Spatial Vision Transformer for satellite image time series analysis

• rice-irrigation-mapping-s1s2 -> Mapping rice irrigation using Sentinel-1 and Sentinel-2 data

Crop classification

(left) false colour image and (right) the crop map.

Crop classification in remote sensing is the identification and mapping of different crops in images or sequences of images. It aims to provide insight into the distribution and composition of crops in a specific area, with applications that include monitoring crop growth and evaluating crop damage. Both traditional machine learning methods, such as decision trees and support vector machines, and deep learning techniques, such as convolutional neural networks (CNNs), can be used to perform crop classification. The optimal method depends on the size and complexity of the dataset, the desired accuracy, and the available computational resources. However, the success of crop classification relies heavily on the quality and resolution of the input data, as well as the availability of labeled training data. Image source: High resolution satellite imaging sensors for precision agriculture by Chenghai Yang

• Classification of Crop Fields through Satellite Image Time Series -> using a pytorch-psetae & Sentinel-2 data

• CropDetectionDL -> using GRU-net, First place solution for Crop Detection from Satellite Imagery competition organized by CV4A workshop at ICLR 2020

• 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

• Crop-Classification -> crop classification using multi temporal satellite images

• 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

• timematch -> 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

• elects -> End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping

• 3d-fpn-and-time-domain -> Sentinel 2 Time Series Analysis with 3D Feature Pyramid Network and Time Domain Class Activation Intervals for Crop Mapping

• in-season-and-dynamic-crop-mapping -> In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time series, uses the Lombardy crop dataset

• MultiviewCropClassification -> A COMPARATIVE ASSESSMENT OF MULTI-VIEW FUSION LEARNING FOR CROP CLASSIFICATION

• Detection of manure application on crop fields leveraging satellite data and Machine Learning

• StressNet: A spatial-spectral-temporal deformable attention-based framework for water stress classification in maize -> Water Stress Classification on Multispectral data of Maize captured by UAV

• model_ecaas_agrifieldnet_gold -> AgriFieldNet Model for Crop Types Detection. First place solution of the of the Zindi AgriFieldNet India Challenge for Crop Types Detection from Satellite Imagery.

• H2Crop -> Fine-grained Hierarchical Crop Type Classification from Integrated Hyperspectral EnMAP Data and Multispectral Sentinel-2 Time Series: A Large-scale Dataset and Dual-stream Transformer Method

• Mask-PSTIN -> Improving crop type mapping by integrating LSTM with temporal random masking and pixel-set spatial information

• Self-Attention for Raw Optical Satellite Time Series Classification and Explaining Attention with Domain Knowledge

• T3S -> code for paper: T³S: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series. A model-agnostic, phenology-aware method that uses cumulative growing degree days to improve crop mapping across years and regions.

• SwissCrop25 -> code and dataset for paper: SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mapping

Crop yield & vegetation forecasting

Wheat yield data. Blue vertical lines denote observation dates.

Crop yield is a crucial metric in agriculture, as it determines the productivity and profitability of a farm. It is defined as the amount of crops produced per unit area of land and is influenced by a range of factors including soil fertility, weather conditions, the type of crop grown, and pest and disease control. By utilizing time series of satellite images, it is possible to perform accurate crop type classification and take advantage of the seasonal variations specific to certain crops. This information can be used to optimize crop management practices and ultimately improve crop yield. However, to achieve accurate results, it is essential to consider the quality and resolution of the input data, as well as the availability of labeled training data. Appropriate pre-processing and feature extraction techniques must also be employed. Image source.

• Crop yield Prediction with Deep Learning -> Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data. PyTorch implementation and an extension for soybean crop forecasting in Argentina

• Deep-Transfer-Learning-Crop-Yield-Prediction

• SPACY -> Satellite Prediction of Aggregate Corn Yield

• CNN-RNN-Yield-Prediction ->A CNN-RNN Framework for Crop Yield Prediction

• MMST-ViT -> Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer. This paper utilizes the Tiny CropNet dataset of county-level crop yield predictions

• Greenearthnet -> Multi-modal learning for geospatial vegetation forecasting

• crop-forecasting -> Predicting rice field yields

• SICKLE -> A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters. Basline solutions: U-TAE, U-Net3D and ConvLSTM

• yieldCNN -> Training temporal Convolution Neural Networks (CNNs) on satellite image time series for yield forecasting

• Pixel-based yield mapping and prediction from Sentinel-2 using spectral indices and neural networks

• Predicting Crop Yield Lows Through Highs via Binned Deep Imbalanced Regression

• List of CNNs in Agriculture

• DeepYield -> A combined convolutional neural network with long short-term memory for crop yield forecasting

• CropMOSAIKS Crop Modeling -> predicting crop yields in Zambia using MOSAIKS

• UniCrop -> a configuration-driven, universal data pipeline designed to automate the construction of analysis-ready environmental datasets for crop yield modelling

• rs-spatiotemporal-vineyard-yield-forecasting -> Spatiotemporal vineyard yield forecasting using satellite imagery (Sentinel 2) and management practice data

• AgriGuard: Multi-Modal Crop Disease Detection System -> Multi-spectral satellite data analysis system combining Sentinel-2 imagery with deep learning for early crop disease detection in precision agriculture applications.

• cleanRfield -> a compilation of functions to clean and filter observations from yield monitors or other agricultural spatial point data.

• Yield-Loss -> Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction

• VITA -> Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecasting

• Bayesian-posterior-based-EnKF -> The improved winter wheat yield estimation by assimilating GLASS LAI into a crop growth model with the proposed Bayesian posterior-based ensemble Kalman filter.

• PBCNN -> A Phenology-guided Bayesian-CNN (PB-CNN) framework for soybean yield estimation and uncertainty analysis.

• imbalance_deep_regression_yield_forecasting -> Predicting Crop Yield Lows Through Highs via Binned Deep Imbalanced Regression

• YieldSAT -> A Multimodal Benchmark Dataset for High-Resolution Crop Yield Prediction.

• Yield Africa -> Do Foundation Model Embeddings Improve Cross-Country Crop Yield Generalisation? A Leave-One-Country-Out Evaluation in Sub-Saharan Africa

• Transfer Learning for Cross-Regional Soybean Yield Prediction

• Sentinel-Yield -> Unsupervised agricultural anomaly detection using satellite foundation model embeddings.

• Cotton-Yield-Forecast-2025 -> LSTM for Multi-Source Cotton Yield Estimation and Temporal Interpretability Across Agro-Ecological Regions in Türkiye

• OmniTerra: Global Yield Intelligence -> a Multi-Modal Spatio-Temporal Transformer (ST-Transformer) framework for global crop yield intelligence and carbon sequestration modelling

• HarvestSight -> Geospatial-AI corn yield forecasting for the U.S. Corn Belt. Fine-tuning NASA/IBM Prithvi-EO-2.0-600M with LoRA, fused with weather/soil/drought features and calibrated uncertainty cones.

• PhenoYieldNet

• CropFusionNet -> an interpretable deep learning framework for probabilistic crop yield forecasting in Germany that fuses satellite, climate, soil, and topographic data

Wealth and economic activity

COVID-19 impacts on human and economic activities.

The traditional approach of collecting economic data through ground surveys is a time-consuming and resource-intensive process. However, advancements in satellite technology and machine learning offer an alternative solution. By utilizing satellite imagery and applying machine learning algorithms, it is possible to obtain accurate and current information on economic activity with greater efficiency. This shift towards satellite imagery-based forecasting not only provides cost savings but also offers a wider and more comprehensive perspective of economic activity. As a result, it is poised to become a valuable asset for both policymakers and businesses. Image source.

• 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

• satellite_led_liverpool -> Remote Sensing-Based Measurement of Living Environment Deprivation - Improving Classical Approaches with Machine Learning

• Predicting_Energy_Consumption_With_Convolutional_Neural_Networks

• SustainBench -> Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning

• Measuring the Impacts of Poverty Alleviation Programs with Satellite Imagery and Deep Learning

• deeppop -> Deep Learning Approach for Population Estimation from Satellite Imagery, also on Github

• Estimating telecoms demand in areas of poor data availability

• 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 -> Learning to score economic development from satellite imagery

• READ -> 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](https://github