{"id":18497372,"url":"https://github.com/satellite-image-deep-learning/datasets","last_synced_at":"2026-02-27T10:33:01.125Z","repository":{"id":65527731,"uuid":"570460643","full_name":"satellite-image-deep-learning/datasets","owner":"satellite-image-deep-learning","description":"Datasets for deep learning with satellite \u0026 aerial 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["datasets","earth-observation","remote-sensing","satellite-data","satellite-imagery","sentinel"],"created_at":"2024-11-06T13:34:18.663Z","updated_at":"2026-02-27T10:33:01.104Z","avatar_url":"https://github.com/satellite-image-deep-learning.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n  \u003cp\u003e\n    \u003ca href=\"https://www.satellite-image-deep-learning.com/\"\u003e\n        \u003cimg src=\"logo.png\" width=\"700\"\u003e\n    \u003c/a\u003e\n\u003c/p\u003e\n  \u003ch2\u003eDatasets for deep learning applied to satellite and aerial imagery.\u003c/h2\u003e\n\n# 👉 [satellite-image-deep-learning.com](https://www.satellite-image-deep-learning.com/) 👈\n\n\u003c/div\u003e\n\n**How to use this repository:** if you know exactly what you are looking for (e.g. you have the paper name) you can `Control+F` to search for it in this page (or search in the raw markdown).\n\n# Lists of datasets\n\u003c!-- markdown-link-check-disable --\u003e\n* [Earth Observation Database](https://eod-grss-ieee.com/)\n\u003c!-- markdown-link-check-enable --\u003e\n* [awesome-satellite-imagery-datasets](https://github.com/chrieke/awesome-satellite-imagery-datasets)\n* [Awesome_Satellite_Benchmark_Datasets](https://github.com/Seyed-Ali-Ahmadi/Awesome_Satellite_Benchmark_Datasets)\n* [awesome-remote-sensing-change-detection](https://github.com/wenhwu/awesome-remote-sensing-change-detection) -\u003e dedicated to change detection\n* [Callisto-Dataset-Collection](https://github.com/Agri-Hub/Callisto-Dataset-Collection) -\u003e datasets that use Copernicus/sentinel data\n* [geospatial-data-catalogs](https://github.com/giswqs/geospatial-data-catalogs) -\u003e A list of open geospatial datasets available on AWS, Earth Engine, Planetary Computer, and STAC Index\n* [BED4RS](https://captain-whu.github.io/BED4RS/)\n* [Satellite-Image-Time-Series-Datasets](https://github.com/corentin-dfg/Satellite-Image-Time-Series-Datasets)\n\n# Remote sensing dataset hubs\n* [Radiant MLHub](https://mlhub.earth/) -\u003e both datasets and models\n* [Registry of Open Data on AWS](https://registry.opendata.aws)\n* [Microsoft Planetary Computer data catalog](https://planetarycomputer.microsoft.com/catalog)\n* [Google Earth Engine Data Catalog](https://developers.google.com/earth-engine/datasets)\n\n## Sentinel\nAs part of the [EU Copernicus program](https://en.wikipedia.org/wiki/Copernicus_Programme), multiple Sentinel satellites are capturing imagery -\u003e see [wikipedia](https://en.wikipedia.org/wiki/Copernicus_Programme#Sentinel_missions)\n\n### Sentinel-1 (SAR)\n* [Xarray backend to Copernicus Sentinel-1 satellite data products](https://github.com/bopen/xarray-sentinel)\n* [mmflood](https://github.com/edornd/mmflood) -\u003e Flood delineation from Sentinel-1 SAR imagery, with [paper](https://ieeexplore.ieee.org/abstract/document/9882096)\n* [Sentinel-1 for Science Amazonas](https://sen4ama.gisat.cz/) -\u003e forest lost time series dataset\n\n### Sentinel-2 (Optical)\n* [Sentinel-2 Cloud-Optimized GeoTIFFs](https://registry.opendata.aws/sentinel-2-l2a-cogs/) and [Sentinel-2 L2A 120m Mosaic](https://registry.opendata.aws/sentinel-s2-l2a-mosaic-120/)\n* [Open access data on GCP](https://console.cloud.google.com/storage/browser/gcp-public-data-sentinel-2?prefix=tiles%2F31%2FT%2FCJ%2F)\n* [Example loading sentinel data in a notebook](https://github.com/binder-examples/getting-data/blob/master/Sentinel2.ipynb)\n* [Analyzing Sentinel-2 satellite data in Python with Keras](https://github.com/jensleitloff/CNN-Sentinel)\n* [SEN2VENµS](https://zenodo.org/record/6514159#.YoRxM5PMK3I) -\u003e a dataset for the training of Sentinel-2 super-resolution algorithms\n* [M3LEO](https://huggingface.co/M3LEO) -\u003e [Github](https://github.com/spaceml-org/M3LEO). A very large scale georeferenced dataset of Sentinel 1/2 imagery plus interferometric SAR products and auxiliary datasets such as Land cover, Biomass and Digital Elevation Models.\n* [SEN12MS](https://github.com/zhu-xlab/SEN12MS) -\u003e A Curated Dataset of Georeferenced Multi-spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion. Checkout [SEN12MS toolbox](https://github.com/schmitt-muc/SEN12MS) and many referenced uses on [paperswithcode.com](https://paperswithcode.com/dataset/sen12ms)\n* [SEN2NAIP](https://huggingface.co/datasets/tacofoundation/SEN2NAIPv2) -\u003e Spatially and spectrally harmonized Sen-2 + NAIP dataset for 4x RGB-NIR super-resolution.\n* [Sen4AgriNet](https://github.com/Orion-AI-Lab/S4A) -\u003e A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning, with and [models](https://github.com/Orion-AI-Lab/S4A-Models)\n* [sentinel2tools](https://github.com/QuantuMobileSoftware/sentinel2tools) -\u003e downloading \u0026 basic processing of Sentinel 2 imagesry. Read [Sentinel2tools: simple lib for downloading Sentinel-2 satellite images](https://medium.com/geekculture/sentinel2tools-simple-lib-for-downloading-sentinel-2-satellite-images-f8a6be3ee894)\n* [open-sentinel-map](https://github.com/VisionSystemsInc/open-sentinel-map) -\u003e The OpenSentinelMap dataset contains Sentinel-2 imagery and per-pixel semantic label masks derived from OpenStreetMap\n* [Canadian-cropland-dataset](https://github.com/bioinfoUQAM/Canadian-cropland-dataset) -\u003e a novel patch-based dataset compiled using optical satellite images of Canadian agricultural croplands retrieved from Sentinel-2\n* [Sentinel-2 Cloud Cover Segmentation Dataset](https://mlhub.earth/data/ref_cloud_cover_detection_challenge_v1) on Radiant mlhub\n* [The Azavea Cloud Dataset](https://www.azavea.com/blog/2021/08/02/the-azavea-cloud-dataset/) which is used to train this [cloud-model](https://github.com/azavea/cloud-model)\n* [fMoW-Sentinel](https://purl.stanford.edu/vg497cb6002) -\u003e The Functional Map of the World - Sentinel-2 corresponding images (fMoW-Sentinel) dataset consists of image time series collected by the Sentinel-2 satellite, corresponding to locations from the Functional Map of the World (fMoW) dataset across several different times. Used in [SatMAE](https://github.com/sustainlab-group/SatMAE)\n* [Earth Surface Water Dataset](https://zenodo.org/record/5205674#.Y4iEFezP1hE) -\u003e a dataset for deep learning of surface water features on Sentinel-2 satellite images. See [this ref using it in torchgeo](https://towardsdatascience.com/artificial-intelligence-for-geospatial-analysis-with-pytorchs-torchgeo-part-1-52d17e409f09)\n* [Ship-S2-AIS dataset](https://zenodo.org/record/7229756#.Y5GsgOzP1hE) -\u003e 13k tiles extracted from 29 free Sentinel-2 products. 2k images showing ships in Denmark sovereign waters: one may detect cargos, fishing, or container ships\n* [Amazon Rainforest dataset for semantic segmentation](https://zenodo.org/record/3233081#.Y6LPLOzP1hE) -\u003e Sentinel 2 images\n* [MATTER](https://github.com/periakiva/MATTER) -\u003e a Sentinel 2 dataset for Self-Supervised Training\n* [S2GLC](https://s2glc.cbk.waw.pl/) -\u003e High resolution Land Cover Map of Europe\n* [Generating Imperviousness Maps from Multispectral Sentinel-2 Satellite Imagery](https://zenodo.org/record/7058860#.ZDrAeuzMLdo)\n* [Sentinel-2 Water Edges Dataset (SWED)](https://openmldata.ukho.gov.uk/)\n* [Sentinel2 Munich480](https://www.kaggle.com/datasets/artelabsuper/sentinel2-munich480) -\u003e dataset for crop mapping by exploiting the time series of Sentinel-2 satellite\n* [Meadows vs Orchards](https://www.kaggle.com/datasets/baptistel/meadows-vs-orchards) -\u003e a pixel time series dataset\n* [Sentinel-2 Image Time Series for Crop Mapping](https://www.kaggle.com/datasets/ignazio/sentinel2-crop-mapping) -\u003e data for the Lombardy region in Italy\n* [Deforestation in Ukraine from Sentinel2 data](https://www.kaggle.com/datasets/isaienkov/deforestation-in-ukraine)\n* [satellite-change-events](https://www.cs.cornell.edu/projects/satellite-change-events/) -\u003e CaiRoad \u0026 CalFire change detection Sentinel 2 datasets\n* [Sentinel-2 dataset for ship detection](https://zenodo.org/records/3923841), also edited and redistributed as [VDS2RAW](https://zenodo.org/records/7982468#.ZIiLxS8QOo4)\n* [MineSegSAT](https://github.com/macdonaldezra/MineSegSAT) -\u003e dataset for paper: AN AUTOMATED SYSTEM TO EVALUATE MINING DISTURBED AREA EXTENTS FROM SENTINEL-2 IMAGERY\n* [CaBuAr](https://github.com/DarthReca/CaBuAr) -\u003e California Burned Areas dataset for delineation\n* [sen12mscr](https://patricktum.github.io/cloud_removal/sen12mscr/) -\u003e Multimodal Cloud Removal\n* [Greenearthnet](https://github.com/vitusbenson/greenearthnet/tree/main) -\u003e dataset specifically designed for high-resolution vegetation forecasting\n* [Floating-Marine-Debris-Data](https://github.com/miguelmendesduarte/Floating-Marine-Debris-Data) -\u003e floating marine debris, with annotations for six debris classes, including plastic, driftwood, seaweed, pumice, sea snot, and sea foam.\n* [Sen2Fire](https://zenodo.org/records/10881058) -\u003e A Challenging Benchmark Dataset for Wildfire Detection using Sentinel Data\n* [L1BSR](https://zenodo.org/records/7826696) -\u003e 3740 pairs of overlapping image crops extracted from two L1B products\n* [GloSoFarID](https://github.com/yzyly1992/GloSoFarID) -\u003e Global multispectral dataset for Solar Farm IDentification\n* [MARIDA](https://marine-debris.github.io/index.html) -\u003e Marine Debris detection from Sentinel-2\n* [MADOS](https://github.com/gkakogeorgiou/mados) -\u003e Marine Debris and Oil Spill from Sentinel-2\n* [Sentinel-2 dataset for ship detection and characterization](https://zenodo.org/records/10418786) -\u003e RGB\n* [S2-SHIPS](https://github.com/alina2204/contrastive_SSL_ship_detection) -\u003e all 12 channels\n* [ChatEarthNet](https://github.com/zhu-xlab/ChatEarthNet) -\u003e A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models, utilizes Sentinel-2 data with captions generated by ChatGPT\n* [UKFields](https://github.com/Spiruel/UKFields) -\u003e over 2.3 million automatically delineated field boundaries spanning England, Wales, Scotland, and Northern Ireland\n* [ShipWakes](https://zenodo.org/records/7947694) -\u003e Keypoints Method for Recognition of Ship Wake Components in Sentinel-2 Images by Deep Learning\n* [TimeSen2Crop](https://zenodo.org/records/4715631) -\u003e a Million Labeled Samples Dataset of Sentinel 2 Image Time Series for Crop Type Classification\n* [AgriSen-COG](https://github.com/tselea/agrisen-cog) -\u003e a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping: includes an anomaly detection preprocessing step\n* [MagicBathyNet](https://www.magicbathy.eu/magicbathynet.html) -\u003e a new multimodal benchmark dataset made up of image patches of Sentinel-2, SPOT-6 and aerial imagery, bathymetry in raster format and seabed classes annotations\n* [MuS2: A Benchmark for Sentinel-2 Multi-Image Super-Resolution](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.7910%2FDVN%2F1JMRAT)\n* [Sen4Map](https://datapub.fz-juelich.de/sen4map/) -\u003e Sentinel-2 time series images, covering over 335,125 geo-tagged locations across the European Union. These geo-tagged locations are associated with detailed landcover and land-use information\n* [CloudSEN12Plus](https://huggingface.co/datasets/isp-uv-es/CloudSEN12Plus) -\u003e the largest cloud detection dataset to date for Sentinel-2\n* [mayrajeo S2 ship detection](https://github.com/mayrajeo/ship-detection) -\u003e labels for Detecting marine vessels from Sentinel-2 imagery with YOLOv8\n* [Fields of The World](https://fieldsofthe.world/) -\u003e instance segmentation of agricultural field boundaries\n* [ai4boundaries](https://github.com/waldnerf/ai4boundaries) -\u003e field boundaries with Sentinel-2 and aerial photography\n* [California Wildfire GeoImaging Dataset - CWGID](https://arxiv.org/abs/2409.16380) -\u003e Development and Application of a Sentinel-2 Satellite Imagery Dataset for Deep-Learning Driven Forest Wildfire Detection\n* [substation-seg](https://github.com/Lindsay-Lab/substation-seg) -\u003e segmenting substations dataset\n* [PhilEO-downstream](https://huggingface.co/datasets/PhilEO-community/PhilEO-downstream) -\u003e a 400GB Sentinel-2 dataset for building density estimation, road segmentation, and land cover classification.\n* [PhilEO-pretrain](https://huggingface.co/datasets/PhilEO-community/PhilEO-pretrain) -\u003e a 500GB global dataset of Sentinel-2 images for model pre-training.\n* [KappaSet: Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks](https://zenodo.org/records/7100327)\n* [AllClear](https://allclear.cs.cornell.edu/) A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery\n* [Sentinel-2 reference cloud masks generated by an active learning method](https://zenodo.org/records/1460961)\n* [Cloud gap-filling with deep learning for improved grassland monitoring](https://zenodo.org/records/11651601)\n* [Remote Sensing Ship Wake Dataset](https://github.com/zjze/RSSW_Dateset)\n* [ERAS-dataset](https://github.com/cscribano/ERAS-dataset) -\u003e Emilia-Romagna Agri Seg (ERAS) field segmentation dataset\n* [Sentinel 2 super-resolved data cubes - 92 scenes over 2 regions in Switzerland spanning 5 years](https://ieee-dataport.org/documents/sentinel-2-super-resolved-data-cubes-92-scenes-over-2-regions-switzerland-spanning-5-years)\n* [SeasoNet](https://zenodo.org/records/6979994) -\u003e A Seasonal Scene Classification, Segmentation and Retrieval Dataset for Satellite Imagery over Germany. Land cover classes based on the CORINE Land Cover database (CLC) 2018\n* [EuroCropsML](https://github.com/dida-do/eurocropsml) -\u003e a ready-to-use benchmark dataset for few-shot crop type classification using Sentinel-2 imagery\n* [CanadaFireSat](https://github.com/eceo-epfl/CanadaFireSat-Data) -\u003e Sentinel-2 Level-1C time series\n* [ssl4eco](https://github.com/PlekhanovaElena/ssl4eco) -\u003e a recipe for building pretraining sets that capture the geographical and phenological diversity of ecosystems across the globe\n* [IRRISIGHT](https://github.com/Nibir088/IRRISIGHT) -\u003e a large-scale, multimodal remote sensing dataset for irrigation classification, soil-water mapping, and agricultural monitoring.\n* [SentinelKilnDB](https://sustainability-lab.github.io/sentinelkilndb/) -\u003e Sentinel-2 dataset for monitoring brick kiln emissions in South Asia\n* [MSSWD - Multi-Spectral Ship Wake Dataset](https://zenodo.org/records/13870226)\n* [MOSAIC-SEN2-CC](https://github.com/ChangeCapsInRS/MOSAIC-SEN2-CC) -\u003e A Multispectral Dataset and Adaptation Framework for Remote Sensing Change Captioning\n* [PLUTo](https://zenodo.org/records/15629667) -\u003e post-deforestation land uses across the tropics\n* [SentinelKilnDB](https://github.com/rishabh-mondal/SENTINELKILNDB_NeurIPS_2025) -\u003e A Large-Scale Dataset and Benchmark for Oriented Bounding Box (OBB) Brick Kiln Detection in South Asia Using Satellite Imagery\n* [GSDD](https://zenodo.org/records/17161810) -\u003e Global Supraglacial Debris Dataset\n* [MT4AFE](https://zenodo.org/records/15395167) -\u003e Multi-Task Learning for Agricultural Field Extraction\n\n### Combined Sentinel\n* [awesome-sentinel](https://github.com/Fernerkundung/awesome-sentinel) -\u003e a curated list of awesome tools, tutorials and APIs related to data from the Copernicus Sentinel Satellites.\n* Paid access to Sentinel \u0026 Landsat data via [sentinel-hub](https://www.sentinel-hub.com/) and [python-api](https://github.com/sentinel-hub/sentinelhub-py)\n* [Jupyter Notebooks for working with Sentinel-5P Level 2 data stored on S3](https://github.com/Sentinel-5P/data-on-s3). The data can be browsed [here](https://meeo-s5p.s3.amazonaws.com/index.html#/?t=catalogs)\n* [Sentinel NetCDF data](https://github.com/acgeospatial/Sentinel-5P/blob/master/Sentinel_5P.ipynb)\n* [earthspy](https://github.com/AdrienWehrle/earthspy) -\u003e Monitor and study any place on Earth and in Near Real-Time (NRT) using the Sentinel Hub services developed by the EO research team at Sinergise\n* [Gold Mining and clandestine airstrips datasets](https://github.com/earthrise-media/mining-detector)\n* [Industrial Smoke Plumes](https://zenodo.org/record/4250706)\n* [MARIDA: Marine Debris Archive](https://github.com/marine-debris/marine-debris.github.io)\n* [OMS2CD](https://github.com/Dibz15/OpenMineChangeDetection) -\u003e hand-labelled images for change-detection in open-pit mining areas\n* [coal power plants' emissions](https://transitionzero.medium.com/estimating-coal-power-plant-operation-from-satellite-images-with-computer-vision-b966af56919e) -\u003e a dataset of coal power plants' emissions, including images, metadata and labels.\n* [RapidAI4EO](https://rapidai4eo.radiant.earth/) -\u003e dense time series satellite imagery sampled at 500,000 locations across Europe, comprising S2 \u0026 Planet imagery, with CORINE Land Cover multiclass labels for 2018\n* [MS-HS-BCD-dataset](https://github.com/arcgislearner/MS-HS-BCD-dataset) -\u003e multisource change detection dataset used in paper: Building Change Detection with Deep Learning by Fusing Spectral and Texture Features of Multisource Remote Sensing Images: A GF-1 and Sentinel 2B Data Case\n* [CropNet: An Open Large-Scale Dataset with Multiple Modalities for Climate Change-aware Crop Yield Predictions](https://github.com/fudong03/CropNet) -\u003e terabyte-sized, publicly available, and multi-modal dataset for climate change-aware crop yield predictions\n* [Tiny CropNet dataset](https://github.com/fudong03/MMST-ViT)\n* [Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery](https://zenodo.org/record/5644746)\n* [METER-ML: A Multi-sensor Earth Observation Benchmark for Automated Methane Source Mapping](https://stanfordmlgroup.github.io/projects/meter-ml/) -\u003e data [on Zenodo](https://zenodo.org/record/6911013)\n* [MultiSenGE](https://zenodo.org/records/6375466) -\u003e large-scale multimodal and multitemporal benchmark dataset\n* [SEN12MS](https://github.com/zhu-xlab/SEN12MS) -\u003e A Curated Dataset of Georeferenced Multi-spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion. Checkout [SEN12MS toolbox](https://github.com/schmitt-muc/SEN12MS) and many referenced uses on [paperswithcode.com](https://paperswithcode.com/dataset/sen12ms)\n* [Space2Ground](https://github.com/Agri-Hub/Space2Ground) -\u003e dataset with Space (Sentinel-1/2) and Ground (street-level images) components, annotated with crop-type labels for agriculture monitoring.\n* [MSCDUnet](https://github.com/Lihy256/MSCDUnet) -\u003e change detection datasets containing VHR, multispectral (Sentinel-2) and SAR (Sentinel-1)\n* [OMBRIA](https://github.com/geodrak/OMBRIA) -\u003e Sentinel-1 \u0026 2 dataset for adressing the flood mapping problem\n* [Satellite Burned Area Dataset](https://zenodo.org/record/6597139#.Y9ufiezP1hE) -\u003e segmentation dataset containing several satellite acquisitions related to past forest wildfires. It contains 73 acquisitions from Sentinel-2 and Sentinel-1 (Copernicus).\n* [SEN12_GUM](https://zenodo.org/record/6914898) -\u003e SEN12 Global Urban Mapping Dataset\n* [Sentinel-1\u00262 Image Pairs (SAR \u0026 Optical)](https://www.kaggle.com/datasets/requiemonk/sentinel12-image-pairs-segregated-by-terrain)\n* [MSOSCD](https://github.com/Lihy256/MSCDUnet) -\u003e change detection datasets containing VHR, multispectral (Sentinel-2) and SAR (Sentinel-1)\n* [SICKLE](https://github.com/Depanshu-Sani/SICKLE) -\u003e A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters. Multi-resolution time-series images from Landsat-8, Sentinel-1, and Sentinel-2\n* [Sentinel-1 and Sentinel-2 Vessel Detection](https://github.com/allenai/vessel-detection-sentinels)\n* [TreeSatAI](https://zenodo.org/records/6780578) -\u003e Sentinel-1, Sentinel-2\n* [AI2-S2-NAIP](https://huggingface.co/datasets/allenai/s2-naip) -\u003e aligned NAIP, Sentinel-2, Sentinel-1, and Landsat images spanning the entire continental US\n* [POPCORN: High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2](https://popcorn-population.github.io/)\n* [CropClimateX](https://github.com/drnhhl/CropClimateX) -\u003e A large-scale Multitask, Multisensory Dataset for Crop Monitoring under Climate Extremes\n* [SmallMinesDS](https://huggingface.co/datasets/ellaampy/SmallMinesDS) -\u003e A Multimodal Dataset for Mapping Artisanal and Small-Scale Gold Mines. Imagery reused in [CocoaMiningDS](https://huggingface.co/datasets/ellaampy/CocoaMiningDS)\n* [Hoss-ReID](https://github.com/Alioth2000/Hoss-ReID) -\u003e Cross-modal Ship Re-Identification via Optical and SAR Imagery\n*  [IDEABench Benchmark Dataset](https://github.com/IDEAtlas/ai-dua-mapping) -\u003e Mapping and Benchmarking Urban Deprivation for a Global Sample of Cities\n* [ImpactMesh](https://github.com/IBM/ImpactMesh) -\u003e a large-scale multimodal, multitemporal dataset for flood and wildfire mapping\n* [Sen12Landslides](https://github.com/PaulH97/Sen12Landslides) -\u003e Spatio-Temporal Landslide \u0026 Anomaly Detection Dataset\n\n## Landsat\nLong running US program -\u003e see [Wikipedia](https://en.wikipedia.org/wiki/Landsat_program)\n* 8 bands, 15 to 60 meters, 185km swath, the temporal resolution is 16 days\n* [Landsat 4, 5, 7, and 8 imagery on Google](https://cloud.google.com/storage/docs/public-datasets/landsat), see [the GCP bucket here](https://console.cloud.google.com/storage/browser/gcp-public-data-landsat/), with Landsat 8 imagery in COG format analysed in [this notebook](https://github.com/pangeo-data/pangeo-example-notebooks/blob/master/landsat8-cog-ndvi.ipynb)\n* [Landsat 8 imagery on AWS](https://registry.opendata.aws/landsat-8/), with many tutorials and tools listed\n* https://github.com/kylebarron/landsat-mosaic-latest -\u003e Auto-updating cloudless Landsat 8 mosaic from AWS SNS notifications\n* [Visualise landsat imagery using Datashader](https://examples.pyviz.org/landsat/landsat.html#landsat-gallery-landsat)\n* [Landsat-mosaic-tiler](https://github.com/kylebarron/landsat-mosaic-tiler) -\u003e This repo hosts all the code for landsatlive.live website and APIs.\n* [LandsatSCD](https://github.com/ggsDing/SCanNet/tree/main) -\u003e a change detection dataset, it consists of 8468 pairs of images, each having the spatial resolution of 416 × 416\n* [The Landsat Irish Coastal Segmentation Dataset](https://zenodo.org/records/8414665)\n\n## VENμS\nVegetation and Environment monitoring on a New Micro-Satellite ([VENμS](https://en.wikipedia.org/wiki/VEN%CE%BCS))\n* [VENUS L2A Cloud-Optimized GeoTIFFs](https://registry.opendata.aws/venus-l2a-cogs/)\n* [VENuS cloud mask training dataset](https://zenodo.org/records/7040177)\n* [Sen2Venµs](https://zenodo.org/records/6514159) -\u003e a dataset for the training of Sentinel-2 super-resolution algorithms\n* [sen2venus-pytorch-dataset](https://github.com/piclem/sen2venus-pytorch-dataset) -\u003e torch dataloader and other utilities\n\n## Vantor\nSatellites owned by Vantor (formerly Maxar \u0026 DigitalGlobe) include [GeoEye-1](https://en.wikipedia.org/wiki/GeoEye-1), [WorldView-2](https://en.wikipedia.org/wiki/WorldView-2), [3](https://en.wikipedia.org/wiki/WorldView-3) \u0026 [4](https://en.wikipedia.org/wiki/WorldView-4)\n* [Maxar Open Data Program](https://github.com/opengeos/maxar-open-data) provides pre and post-event high-resolution satellite imagery in support of emergency planning, response, damage assessment, and recovery\n* [WorldView-2 European Cities](https://earth.esa.int/eogateway/catalog/worldview-2-european-cities) -\u003e dataset covering the most populated areas in Europe at 40 cm resolution\n\n## Planet\nAlso see Spacenet-7 and the Kaggle ship and plane classifications datasets later in this page\n* [Planet’s high-resolution, analysis-ready mosaics of the world’s tropics](https://www.planet.com/nicfi/), supported through Norway’s International Climate \u0026 Forests Initiative. [BBC coverage](https://www.bbc.co.uk/news/science-environment-54651453)\n* Planet have made imagery available via kaggle competitions\n* [Alberta Wells Dataset](https://zenodo.org/records/13743323) -\u003e Pinpointing Oil and Gas Wells from Satellite Imagery\n* [ARGO ship classification dataset](https://zenodo.org/records/6058710) -\u003e 1750 labelled images from PlanetScope-4-Band satelites. Created [here](https://github.com/elizamanelli/ship_dataset/tree/main)\n* [Marine Debris Dataset for Object Detection in Planetscope Imagery](https://cmr.earthdata.nasa.gov/search/concepts/C2781412735-MLHUB.html)\n* [LitterLines](https://github.com/geoJoost/LitterLines) -\u003e An Annotated Dataset for Detection of Marine Litter Windrows in PlanetScope Imagery\n* [FloodPlanet Inundation Dataset](https://zenodo.org/records/15238572) -\u003e multi-sensor co-registered dataset labeled based on 3m PlanetScope data and spatially overlapping, temporally near Sentinel-1, Sentinel-2, and Landsat-8 data\n* [Zhijie_FloodPlanet_2023](https://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Zhijie_FloodPlanet_2023) -\u003e 19 flood events that occurred between 2017 and 2020\n\n## UC Merced\nLand use classification dataset with 21 classes and 100 RGB TIFF images for each class. Each image measures 256x256 pixels with a pixel resolution of 1 foot\n* http://weegee.vision.ucmerced.edu/datasets/landuse.html\n* Also [available as a multi-label dataset](https://towardsdatascience.com/multi-label-land-cover-classification-with-deep-learning-d39ce2944a3d)\n* Read [Vision Transformers for Remote Sensing Image Classification](https://www.mdpi.com/2072-4292/13/3/516/htm) where a Vision Transformer classifier achieves 98.49% classification accuracy on Merced\n\n## EuroSAT\nLand use classification dataset of Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples. Available in RGB and 13 band versions\n* [EuroSAT: Land Use and Land Cover Classification with Sentinel-2](https://github.com/phelber/EuroSAT) -\u003e publication where a CNN achieves a classification accuracy 98.57%\n* Repos using fastai [here](https://github.com/shakasom/Deep-Learning-for-Satellite-Imagery) and [here](https://www.luigiselmi.eu/eo/lulc-classification-deeplearning.html)\n* [evolved_channel_selection](http://matpalm.com/blog/evolved_channel_selection/) -\u003e explores the trade off between mixed resolutions and whether to use a channel at all, with [repo](https://github.com/matpalm/evolved_channel_selection)\n* RGB version available as [dataset in pytorch](https://pytorch.org/vision/stable/generated/torchvision.datasets.EuroSAT.html#torchvision.datasets.EuroSAT) with the 13 band version [in torchgeo](https://torchgeo.readthedocs.io/en/latest/api/datasets.html#eurosat). Checkout the tutorial on [data augmentation with this dataset](https://torchgeo.readthedocs.io/en/latest/tutorials/transforms.html)\n* [EuroSAT-SAR](https://huggingface.co/datasets/wangyi111/EuroSAT-SAR) -\u003e matched each Sentinel-2 image in EuroSAT with one Sentinel-1 patch according to the geospatial coordinates\n\n## PatternNet\nLand use classification dataset with 38 classes and 800 RGB JPG images for each class\n* https://sites.google.com/view/zhouwx/dataset?authuser=0\n* Publication: [PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval](https://arxiv.org/abs/1706.03424)\n\n## Gaofen Image Dataset (GID) for classification\n- https://captain-whu.github.io/GID/\n- a large-scale classification set and a fine land-cover classification set\n\n## Million-AID\nA large-scale benchmark dataset containing million instances for RS scene classification, 51 scene categories organized by the hierarchical category\n* https://captain-whu.github.io/DiRS/\n* [Pretrained models](https://github.com/ViTAE-Transformer/ViTAE-Transformer-Remote-Sensing)\n* Also see [AID](https://captain-whu.github.io/AID/), [AID-Multilabel-Dataset](https://github.com/Hua-YS/AID-Multilabel-Dataset) \u0026 [DFC15-multilabel-dataset](https://github.com/Hua-YS/DFC15-Multilabel-Dataset)\n\n## DIOR object detection dataset\nA large-scale benchmark dataset for object detection in optical remote sensing images, which consists of 23,463 images and 192,518 object instances annotated with horizontal bounding boxes\n* https://gcheng-nwpu.github.io/\n* https://arxiv.org/abs/1909.00133\n* [ors-detection](https://github.com/Vlad15lav/ors-detection) -\u003e Object Detection on the DIOR dataset using YOLOv3\n* [dior_detect](https://github.com/hm-better/dior_detect) -\u003e benchmarks for object detection on DIOR dataset\n* [Tools](https://github.com/CrazyStoneonRoad/Tools) -\u003e for dealing with the DIOR\n* [Object_Detection_Satellite_Imagery_Yolov8_DIOR](https://github.com/JohnPPinto/Object_Detection_Satellite_Imagery_Yolov8_DIOR)\n\n## Multiscene\nMultiScene dataset aims at two tasks: Developing algorithms for multi-scene recognition \u0026 Network learning with noisy labels\n* https://multiscene.github.io/ \u0026 https://github.com/Hua-YS/Multi-Scene-Recognition\n\n## FAIR1M object detection dataset\nA Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery\n* [arxiv papr](https://arxiv.org/abs/2103.05569)\n* Download at gaofen-challenge.com\n* [2020Gaofen](https://github.com/AICyberTeam/2020Gaofen) -\u003e 2020 Gaofen Challenge data, baselines, and metrics\n\n## DOTA object detection dataset\nA Large-Scale Benchmark and Challenges for Object Detection in Aerial Images. Segmentation annotations available in iSAID dataset\n* https://captain-whu.github.io/DOTA/index.html\n* [DOTA_devkit](https://github.com/CAPTAIN-WHU/DOTA_devkit) for loading dataset\n* [Arxiv paper](https://arxiv.org/abs/1711.10398)\n* [Pretrained models in mmrotate](https://github.com/open-mmlab/mmrotate)\n* [DOTA2VOCtools](https://github.com/Complicateddd/DOTA2VOCtools) -\u003e dataset split and transform to voc format\n* [dotatron](https://github.com/naivelogic/dotatron) -\u003e 2021 Learning to Understand Aerial Images Challenge on DOTA dataset\n\n## iSAID instance segmentation dataset\nA Large-scale Dataset for Instance Segmentation in Aerial Images\n* https://captain-whu.github.io/iSAID/dataset.html\n* Uses images from the DOTA dataset\n\n## HRSC RGB ship object detection dataset\n* https://www.kaggle.com/datasets/guofeng/hrsc2016\n* [Pretrained models in mmrotate](https://github.com/open-mmlab/mmrotate)\n* [Rotation-RetinaNet-PyTorch](https://github.com/HsLOL/Rotation-RetinaNet-PyTorch)\n\n## SAR Ship Detection Dataset (SSDD)\n* https://github.com/TianwenZhang0825/Official-SSDD\n* [Rotation-RetinaNet-PyTorch](https://github.com/HsLOL/Rotation-RetinaNet-PyTorch)\n\n## High-Resolution SAR Rotation Ship Detection Dataset (SRSDD)\n* [Github](https://github.com/HeuristicLU/SRSDD-V1.0)\n* [A Lightweight Model for Ship Detection and Recognition in Complex-Scene SAR Images](https://www.mdpi.com/2072-4292/14/23/6053)\n\n## LEVIR ship dataset\nA dataset for tiny ship detection under medium-resolution remote sensing images. Annotations in bounding box format\n* [LEVIR-Ship](https://github.com/WindVChen/LEVIR-Ship)\n\u003c!-- markdown-link-check-disable --\u003e\n* Hosted on [Nucleus](https://dashboard.scale.com/nucleus/ds_cbsghny30nf00b1x3w7g?utm_source=open_dataset\u0026utm_medium=github\u0026utm_campaign=levir_ships)\n\u003c!-- markdown-link-check-enable --\u003e\n\n## SAR Aircraft Detection Dataset\n2966 non-overlapped 224×224 slices are collected with 7835 aircraft targets\n* https://github.com/hust-rslab/SAR-aircraft-data\n\n## xView1: Objects in context for overhead imagery\nA fine-grained object detection dataset with 60 object classes along an ontology of 8 class types. Over 1,000,000 objects across over 1,400 km^2 of 0.3m resolution imagery. Annotations in bounding box format\n* [Official website](http://xviewdataset.org/)\n* [arXiv paper](https://arxiv.org/abs/1802.07856).\n* [paperswithcode](https://paperswithcode.com/dataset/xview)\n* [Satellite_Imagery_Detection_YOLOV7](https://github.com/Radhika-Keni/Satellite_Imagery_Detection_YOLOV7) -\u003e YOLOV7 applied to xView1\n\n## xView2: xBD building damage assessment\nAnnotated high-resolution satellite imagery for building damage assessment, precise segmentation masks and damage labels on a four-level spectrum, 0.3m resolution imagery\n* [Official website](https://xview2.org/)\n* [arXiv paper](https://arxiv.org/abs/1911.09296)\n* [paperswithcode](https://paperswithcode.com/paper/xbd-a-dataset-for-assessing-building-damage)\n* [xView2_baseline](https://github.com/DIUx-xView/xView2_baseline) -\u003e baseline solution in tensorflow\n* [metadamagenet](https://github.com/nimaafshar/metadamagenet) -\u003e pytorch solution\n* [U-Net models from michal2409](https://github.com/michal2409/xView2)\n* [DAHiTra](https://github.com/nka77/DAHiTra) -\u003e code for 2022 [paper](https://arxiv.org/abs/2208.02205): Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images. Uses xView2 xBD dataset\n* [Damage assessment using Amazon SageMaker geospatial capabilities and custom SageMaker models](https://aws.amazon.com/blogs/machine-learning/damage-assessment-using-amazon-sagemaker-geospatial-capabilities-and-custom-sagemaker-models/)\n* [Xview2_Strong_Baseline](https://github.com/PaulBorneP/Xview2_Strong_Baseline) -\u003e a simple implementation of a strong baseline\n\n## xView3: Detecting dark vessels in SAR\nDetecting dark vessels engaged in illegal, unreported, and unregulated (IUU) fishing activities on synthetic aperture radar (SAR) imagery. With human and algorithm annotated instances of vessels and fixed infrastructure across 43,200,000 km^2 of Sentinel-1 imagery, this multi-modal dataset enables algorithms to detect and classify dark vessels\n* [Official website](https://iuu.xview.us/)\n* [arXiv paper](https://arxiv.org/abs/2206.00897)\n* [Github](https://github.com/DIUx-xView) -\u003e all reference code, dataset processing utilities, and winning model codes + weights\n* [paperswithcode](https://paperswithcode.com/dataset/xview3-sar)\n* [xview3_ship_detection](https://github.com/naivelogic/xview3_ship_detection)\n\n## Vehicle Detection in Aerial Imagery (VEDAI)\nVehicle Detection in Aerial Imagery. Bounding box annotations\n* https://downloads.greyc.fr/vedai/\n* [pytorch-vedai](https://github.com/MichelHalmes/pytorch-vedai)\n\n## Cars Overhead With Context (COWC)\nLarge set of annotated cars from overhead. Established baseline for object detection and counting tasks. Annotations in bounding box format\n* http://gdo152.ucllnl.org/cowc/\n* https://github.com/LLNL/cowc\n* [Detecting cars from aerial imagery for the NATO Innovation Challenge](https://arthurdouillard.com/post/nato-challenge/)\n* [LINZ and UGRC](https://github.com/humansensinglab/AGenDA/tree/main/Data)\n\n## AI-TOD \u0026 AI-TOD-v2 - tiny object detection\nThe mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than other datasets. Annotations in bounding box format. V2 is a meticulous relabelling of the v1 dataset\n* https://github.com/jwwangchn/AI-TOD\n* https://chasel-tsui.github.io/AI-TOD-v2/\n* [NWD](https://github.com/jwwangchn/NWD) -\u003e code for 2021 [paper](https://arxiv.org/abs/2110.13389): A Normalized Gaussian Wasserstein Distance for Tiny Object Detection. Uses AI-TOD dataset\n* [ORFENet](https://github.com/dyl96/ORFENet) -\u003e Tiny Object Detection in Remote Sensing Images Based on Object Reconstruction and Multiple Receptive Field Adaptive Feature Enhancement. Uses LEVIR-ship \u0026 AI-TOD-v2\n\n## RarePlanes\n* [RarePlanes](https://registry.opendata.aws/rareplanes/) -\u003e incorporates both real and synthetically generated satellite imagery including aircraft. Read the [arxiv paper](https://arxiv.org/abs/2006.02963) and checkout [this repo](https://github.com/jdc08161063/RarePlanes). Note the dataset is available through the AWS Open-Data Program for free download\n* [Understanding the RarePlanes Dataset and Building an Aircraft Detection Model](https://encord.com/blog/rareplane-dataset-aircraft-detection-model/) -\u003e blog post\n* Read [this article from NVIDIA](https://developer.nvidia.com/blog/preparing-models-for-object-detection-with-real-and-synthetic-data-and-tao-toolkit/) which discusses fine tuning a model pre-trained on synthetic data (Rareplanes) with 10% real data, then pruning the model to reduce its size, before quantizing the model to improve inference speed\n* [yoltv4](https://github.com/avanetten/yoltv4) includes examples on the [RarePlanes dataset](https://registry.opendata.aws/rareplanes/)\n* [rareplanes-yolov5](https://github.com/jeffaudi/rareplanes-yolov5) -\u003e using YOLOv5 and the RarePlanes dataset to detect and classify sub-characteristics of aircraft, with [article](https://medium.com/artificialis/detecting-aircrafts-on-airbus-pleiades-imagery-with-yolov5-5f3d464b75ad)\n\n## Counting from Sky\nA Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method\n* https://github.com/gaoguangshuai/Counting-from-Sky-A-Large-scale-Dataset-for-Remote-Sensing-Object-Counting-and-A-Benchmark-Method\n\n## AIRS (Aerial Imagery for Roof Segmentation)\nPublic dataset for roof segmentation from very-high-resolution aerial imagery (7.5cm). Covers almost the full area of Christchurch, the largest city in the South Island of New Zealand.\n* [On Kaggle](https://www.kaggle.com/datasets/atilol/aerialimageryforroofsegmentation)\n* [Rooftop-Instance-Segmentation](https://github.com/MasterSkepticista/Rooftop-Instance-Segmentation) -\u003e VGG-16, Instance Segmentation, uses the Airs dataset\n\n## Inria building/not building segmentation dataset\nRGB GeoTIFF at spatial resolution of 0.3 m. Data covering Austin, Chicago, Kitsap County, Western \u0026 Easter Tyrol, Innsbruck, San Francisco \u0026 Vienna\n* https://project.inria.fr/aerialimagelabeling/contest/\n* [SemSegBuildings](https://github.com/SharpestProjects/SemSegBuildings) -\u003e Project using fast.ai framework for semantic segmentation on Inria building segmentation dataset\n* [UNet_keras_for_RSimage](https://github.com/loveswine/UNet_keras_for_RSimage) -\u003e keras code for binary semantic segmentation\n\n## AICrowd Mapping Challenge: building segmentation dataset\n300x300 pixel RGB images with annotations in COCO format. Imagery appears to be global but with significant fraction from North America\n* Dataset release as part of the [mapping-challenge](https://www.aicrowd.com/challenges/mapping-challenge)\n* Winning solution published by neptune.ai [here](https://github.com/neptune-ai/open-solution-mapping-challenge), achieved precision 0.943 and recall 0.954 using Unet with Resnet.\n* [mappingchallenge](https://github.com/krishanr/mappingchallenge) -\u003e YOLOv5 applied to the AICrowd Mapping Challenge dataset\n\n## BONAI - building footprint dataset\nBONAI (Buildings in Off-Nadir Aerial Images) is a dataset for building footprint extraction (BFE) in off-nadir aerial images\n* https://github.com/jwwangchn/BONAI\n\n## LEVIR-CD building change detection dataset\n* https://justchenhao.github.io/LEVIR/\n* [FCCDN_pytorch](https://github.com/chenpan0615/FCCDN_pytorch) -\u003e pytorch implemention of FCCDN for change detection task\n* [RSICC](https://github.com/Chen-Yang-Liu/RSICC) -\u003e the Remote Sensing Image Change Captioning dataset uses LEVIR-CD imagery\n\n## Onera (OSCD) Sentinel-2 change detection dataset\nIt comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018. \n* [Onera Satellite Change Detection Dataset](https://ieee-dataport.org/open-access/oscd-onera-satellite-change-detection) comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018\n* [Website](https://rcdaudt.github.io/oscd/)\n* [change_detection_onera_baselines](https://github.com/previtus/change_detection_onera_baselines) -\u003e Siamese version of U-Net baseline model\n* [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://github.com/rcdaudt/patch_based_change_detection) -\u003e with [paper](https://ieeexplore.ieee.org/abstract/document/8518015)\n* [DS_UNet](https://github.com/SebastianHafner/DS_UNet) -\u003e code for 2021 paper: Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection using a Dual Stream U-Net, uses Onera Satellite Change Detection dataset\n* [ChangeDetection_wOnera](https://github.com/tonydp03/ChangeDetection_wOnera)\n* [OSCD + additional Dates](https://github.com/granularai/fabric) -\u003e extended with three different dates\n* [MSOSCD](https://github.com/Lihy256/MSCDUnet) -\u003e change detection datasets containing VHR, multispectral (Sentinel-2) and SAR (Sentinel-1)\n\n## SECOND - semantic change detection\n* https://captain-whu.github.io/SCD/\n* Change detection at the pixel level\n\n## Amazon and Atlantic Forest dataset\nFor semantic segmentation with Sentinel 2\n* [Amazon and Atlantic Forest image datasets for semantic segmentation](https://zenodo.org/record/4498086#.Y6LPLuzP1hE)\n* [attention-mechanism-unet](https://github.com/davej23/attention-mechanism-unet) -\u003e An attention-based U-Net for detecting deforestation within satellite sensor imagery\n* [TransUNetplus2](https://github.com/aj1365/TransUNetplus2) -\u003e Rethinking attention gated TransU-Net for deforestation mapping\n\n## Functional Map of the World ( fMoW)\n* https://github.com/fMoW/dataset\n* RGB \u0026 multispectral variants\n* High resolution, chip classification dataset\n* Purpose: predicting the functional purpose of buildings and land use from temporal sequences of satellite images and a rich set of metadata features\n\n## HRSCD change detection\n* https://rcdaudt.github.io/hrscd/\n* 291 coregistered image pairs of high resolution RGB aerial images\n* Pixel-level change and land cover annotations are provided\n\n## MiniFrance-DFC22 - semi-supervised semantic segmentation\n* The [MiniFrance-DFC22 (MF-DFC22) dataset](https://ieee-dataport.org/competitions/data-fusion-contest-2022-dfc2022) extends and modifies the [MiniFrance dataset](https://ieee-dataport.org/open-access/minifrance) for training semi-supervised semantic segmentation models for land use/land cover mapping\n* [dfc2022-baseline](https://github.com/isaaccorley/dfc2022-baseline) -\u003e baseline solution to the 2022 IEEE GRSS Data Fusion Contest (DFC2022) using TorchGeo, PyTorch Lightning, and Segmentation Models PyTorch to train a U-Net with a ResNet-18 backbone and a loss function of Focal + Dice loss to perform semantic segmentation on the DFC2022 dataset\n* https://github.com/mveo/mveo-challenge\n\n## FLAIR\nSemantic segmentation and domain adaptation challenge proposed by the French National Institute of Geographical and Forest Information (IGN). Uses a dataset composed of over 70,000 aerial imagery patches with pixel-based annotations and 50,000 Sentinel-2 satellite acquisitions.\n* [Challenge on codalab](https://codalab.lisn.upsaclay.fr/competitions/13447)\n* [FLAIR-2 github](https://github.com/IGNF/FLAIR-2)\n* [flair-2 8th place solution](https://github.com/association-rosia/flair-2)\n* [IGNF HuggingFace](https://huggingface.co/IGNF)\n\n## ISPRS\nSemantic segmentation dataset. 38 patches of 6000x6000 pixels, each consisting of a true orthophoto (TOP) extracted from a larger TOP mosaic, and a DSM. Resolution 5 cm\n* https://www.isprs.org/education/benchmarks/UrbanSemLab/2d-sem-label-potsdam.aspx\n\n## SpaceNet\nSpaceNet is a series of competitions with datasets and utilities provided. The challenges covered are: (1 \u0026 2) building segmentation, (3) road segmentation, (4) off-nadir buildings, (5) road network extraction, (6) multi-senor mapping, (7) multi-temporal urban change, (8) Flood Detection Challenge Using Multiclass Segmentation\n* [spacenet.ai](https://spacenet.ai/) is an online hub for data, challenges, algorithms, and tools\n* [The SpaceNet 7 Multi-Temporal Urban Development Challenge: Dataset Release](https://medium.com/the-downlinq/the-spacenet-7-multi-temporal-urban-development-challenge-dataset-release-9e6e5f65c8d5)\n* [spacenet-three-topcoder](https://github.com/snakers4/spacenet-three-topcoder) solution\n* [official utilities](https://github.com/SpaceNetChallenge/utilities) -\u003e Packages intended to assist in the preprocessing of SpaceNet satellite imagery dataset to a format that is consumable by machine learning algorithms\n* [andraugust spacenet-utils](https://github.com/andraugust/spacenet-utils) -\u003e Display geotiff image with building-polygon overlay \u0026 label buildings using kNN on the pixel spectra\n* [Spacenet-Building-Detection](https://github.com/IdanC1s2/Spacenet-Building-Detection) -\u003e uses keras and [Spacenet 1 dataset](https://spacenet.ai/spacenet-buildings-dataset-v1/)\n* [Spacenet 8 winners blog post](https://medium.com/@SpaceNet_Project/spacenet-8-a-closer-look-at-the-winning-approaches-75ff4033bf53)\n\n## WorldStrat Dataset\nNearly 10,000 km² of free high-resolution satellite imagery of unique locations which ensure stratified representation of all types of land-use across the world: from agriculture to ice caps, from forests to multiple urbanization densities.\n* https://github.com/worldstrat/worldstrat\n* [Quick tour of the WorldStrat Dataset](https://medium.com/@robmarkcole/quick-tour-of-the-worldstrat-dataset-b2d1c2d435db)\n* Each high-resolution image (1.5 m/pixel) comes with multiple temporally-matched low-resolution images from the freely accessible lower-resolution Sentinel-2 satellites (10 m/pixel)\n* Several super-resolution benchmark models trained on it\n\n## Satlas Pretrain\nSatlasPretrain is a large-scale pre-training dataset for tasks that involve understanding satellite images. Regularly-updated satellite data is publicly available for much of the Earth through sources such as Sentinel-2 and NAIP, and can inform numerous applications from tackling illegal deforestation to monitoring marine infrastructure. \n* [Website](https://satlas-pretrain.allen.ai/)\n* [Code](https://github.com/allenai/satlas)\n\n## FLAIR 1 \u0026 2 Segmentation datasets\n* https://ignf.github.io/FLAIR/\n* The FLAIR #1 semantic segmentation dataset consists of 77,412 high resolution patches (512x512 at 0.2 m spatial resolution) with 19 semantic classes\n* FLAIR #2 includes an expanded dataset of Sentinel-2 time series for multi-modal semantic segmentation\n\n## Five Billion Pixels segmentation dataset\n* https://x-ytong.github.io/project/Five-Billion-Pixels.html\n* 4m Gaofen-2 imagery over China\n* 24 land cover classes\n* Paper and code demonstrating domain adaptation to Sentinel-2 and Planetscope imagery\n* Extends the [GID15 large scale semantic segmentation dataset](https://captain-whu.github.io/GID15/)\n* [GID](https://x-ytong.github.io/project/GID.html) -\u003e the Gaofen Image Dataset is a large-scale land-cover dataset with Gaofen-2 (GF-2) satellite images\n* [MM-5B Dataset](https://github.com/AI-Tianlong/HieraRS) -\u003e Multi-Modal Five-Billion-Pixels is a large-scale, multi-modal, hierarchical Land Cover and Land Use (LCLU) dataset, built upon the Five-Billion-Pixels foundation.\n\n## RF100 object detection benchmark\nRF100 is compiled from 100 real world datasets that straddle a range of domains. The aim is that performance evaluation on this dataset will enable a more nuanced guide of how a model will perform in different domains. Contains 10k aerial images\n* https://www.rf100.org/\n* https://github.com/roboflow-ai/roboflow-100-benchmark\n\n## SATIN (SATellite ImageNet)\nSATIN is a multi-task remote sensing classification metadataset consisting of 27 datasets grouped into 6 tasks. The imagery spans 5 orders of magnitude of resolution, over 250 distinct class labels, and many field of view sizes. The overall SATIN benchmark, as well as each of the 27 constituent datasets, are released via HuggingFace. A public leaderboard is provided to guide and track the progress of vision-language models on SATIN. \n* [Paper](https://arxiv.org/abs/2304.11619)\n* [Website](https://satinbenchmark.github.io/)\n* [Data](https://huggingface.co/datasets/jonathan-roberts1/SATIN)\n\n## SODA-A rotated bounding boxes\n* https://shaunyuan22.github.io/SODA/\n* SODA-A comprises 2513 high-resolution images of aerial scenes, which has 872069 instances annotated with oriented rectangle box annotations over 9 classes\n* https://github.com/shaunyuan22/CFINet\n\n## EarthView from Satellogic\n* https://huggingface.co/datasets/satellogic/EarthView\n* Dataset for foundational models, with Sentinel 1 \u0026 2 and 1m RGB\n\n## Microsoft datasets\n* [US Building Footprints](https://github.com/Microsoft/USBuildingFootprints) -\u003e building footprints in all 50 US states, GeoJSON format, generated using semantic segmentation. Also [Australia](https://github.com/microsoft/AustraliaBuildingFootprints), [Canadian](https://github.com/Microsoft/CanadianBuildingFootprints), [Uganda-Tanzania](https://github.com/microsoft/Uganda-Tanzania-Building-Footprints), [Kenya-Nigeria](https://github.com/microsoft/KenyaNigeriaBuildingFootprints) and [GlobalMLBuildingFootprints](https://github.com/microsoft/GlobalMLBuildingFootprints) are available. Checkout [RasterizingBuildingFootprints](https://github.com/mehdiheris/RasterizingBuildingFootprints) to convert vector shapefiles to raster layers\n* [Microsoft Planetary Computer](https://planetarycomputer.microsoft.com/) is a Dask-Gateway enabled JupyterHub deployment focused on supporting scalable geospatial analysis, [source repo](https://github.com/microsoft/planetary-computer-hub)\n* [landcover-orinoquia](https://github.com/microsoft/landcover-orinoquia) -\u003e Land cover mapping of the Orinoquía region in Colombia, in collaboration with Wildlife Conservation Society Colombia. An #AIforEarth project\n* [RoadDetections dataset by Microsoft](https://github.com/microsoft/RoadDetections)\n\n## Google datasets\n* [open-buildings](https://sites.research.google/open-buildings/) -\u003e A dataset of building footprints to support social good applications covering 64% of the African continent. Read [Mapping Africa’s Buildings with Satellite Imagery](https://ai.googleblog.com/2021/07/mapping-africas-buildings-with.html)\n\n## Google Earth Engine (GEE)\nSince there is a whole community around GEE I will not reproduce it here but list very select references. Get started at https://developers.google.com/earth-engine/\n* Various imagery and climate datasets, including Landsat \u0026 Sentinel imagery\n* Supports large scale processing with classical algorithms, e.g. clustering for land use. For deep learning, you export datasets from GEE as tfrecords, train on your preferred GPU platform, then upload inference results back to GEE\n* [awesome-google-earth-engine](https://github.com/gee-community/awesome-google-earth-engine)\n* [Awesome-GEE](https://github.com/giswqs/Awesome-GEE)\n* [awesome-earth-engine-apps](https://github.com/philippgaertner/awesome-earth-engine-apps)\n* [How to Use Google Earth Engine and Python API to Export Images to Roboflow](https://blog.roboflow.com/how-to-use-google-earth-engine-with-roboflow/) -\u003e to acquire training data\n* [ee-fastapi](https://github.com/csaybar/ee-fastapi) is a simple FastAPI web application for performing flood detection using Google Earth Engine in the backend.\n* [How to Download High-Resolution Satellite Data for Anywhere on Earth](https://towardsdatascience.com/how-to-download-high-resolution-satellite-data-for-anywhere-on-earth-5e6dddee2803)\n* [wxee](https://github.com/aazuspan/wxee) -\u003e Export data from GEE to xarray using wxee then train with pytorch or tensorflow models. Useful since GEE only suports tfrecord export natively\n\n## Image captioning datasets\n* [RSICD](https://github.com/201528014227051/RSICD_optimal) -\u003e 10921 images with five sentences descriptions per image. Used in  [Fine tuning CLIP with Remote Sensing (Satellite) images and captions](https://huggingface.co/blog/fine-tune-clip-rsicd), models at [this repo](https://github.com/arampacha/CLIP-rsicd)\n* [RSICC](https://github.com/Chen-Yang-Liu/RSICC) -\u003e the Remote Sensing Image Change Captioning dataset contains 10077 pairs of bi-temporal remote sensing images and 50385 sentences describing the differences between images. Uses LEVIR-CD imagery\n* [ChatEarthNet](https://github.com/zhu-xlab/ChatEarthNet) -\u003e A Global-Scale Image-Text Dataset Empowering Vision-Language Geo-Foundation Models, utilizes Sentinel-2 data with captions generated by ChatGPT\n\n## Weather Datasets\n* NASA (make request and emailed when ready) -\u003e https://search.earthdata.nasa.gov\n* NOAA (requires BigQuery) -\u003e https://www.kaggle.com/datasets/noaa/goes16/home\n* Time series weather data for several US cities -\u003e https://www.kaggle.com/datasets/selfishgene/historical-hourly-weather-data\n* [DeepWeather](https://github.com/adamhazimeh/DeepWeather) -\u003e improve weather forecasting accuracy by analyzing satellite images\n\n## Cloud datasets\n* [Planet-CR](https://github.com/zhu-xlab/Planet-CR) -\u003e A Multi-Modal and Multi-Resolution Dataset for Cloud Removal in High Resolution Optical Remote Sensing Imagery, 3m resolution, with [paper](https://arxiv.org/abs/2301.03432)\n* [The Azavea Cloud Dataset](https://www.azavea.com/blog/2021/08/02/the-azavea-cloud-dataset/) which is used to train this [cloud-model](https://github.com/azavea/cloud-model)\n* [Sentinel-2 Cloud Cover Segmentation Dataset](https://mlhub.earth/data/ref_cloud_cover_detection_challenge_v1) on Radiant mlhub\n* [cloudsen12](https://cloudsen12.github.io/) -\u003e see [video](https://youtu.be/GhQwnVhJ1wo)\n* [WHUS2-CD+](https://zenodo.org/record/5511793) -\u003e 36 manually labeled cloud masks at 10m resolution and corresponding Sentinel-2 images evenly distributed over China mainland, and used to train CD-FM3SF [cloud-model](https://github.com/Neooolee/WHUS2-CD)\n* [HRC_WHU](https://github.com/dr-lizhiwei/HRC_WHU) -\u003e High-Resolution Cloud Detection Dataset comprising 150 RGB images and a resolution varying from 0.5 to 15 m in different global regions\n* [AIR-CD](https://github.com/AICyberTeam/AIR-CD) -\u003e a challenging cloud detection data set called AIR-CD, with higher spatial resolution and more representative landcover types\n* [Landsat 8 Cloud Cover Assessment Validation Data](https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data)\n\n## Forest datasets\n* [OpenForest](https://github.com/RolnickLab/OpenForest) -\u003e A catalogue of open access forest datasets\n* [awesome-forests](https://github.com/blutjens/awesome-forests) -\u003e A curated list of ground-truth forest datasets for the machine learning and forestry community\n* [ReforesTree](https://github.com/gyrrei/ReforesTree) -\u003e A dataset for estimating tropical forest biomass based on drone and field data\n* [yosemite-tree-dataset](https://github.com/nightonion/yosemite-tree-dataset) -\u003e a benchmark dataset for tree counting from aerial images\n* [Amazon Rainforest dataset for semantic segmentation](https://zenodo.org/record/3233081#.Y6LPLOzP1hE) -\u003e Sentinel 2 images. Used in the paper 'An attention-based U-Net for detecting deforestation within satellite sensor imagery'\n* [Amazon and Atlantic Forest image datasets for semantic segmentation](https://zenodo.org/record/4498086#.Y6LPLuzP1hE) -\u003e Sentinel 2 images. Used in paper 'An attention-based U-Net for detecting deforestation within satellite sensor imagery'\n* [TreeSatAI](https://zenodo.org/records/6780578) -\u003e Sentinel-1, Sentinel-2\n* [PureForest](https://huggingface.co/datasets/IGNF/PureForest) -\u003e VHR RGB + Near-Infrared \u0026 lidar, each patch represents a monospecific forest\n\n## Geospatial datasets\n* [Resource Watch](https://resourcewatch.org/data/explore) provides a wide range of geospatial datasets and a UI to visualise them\n\n## Time series \u0026 change detection datasets\n* [BreizhCrops](https://github.com/dl4sits/BreizhCrops) -\u003e A Time Series Dataset for Crop Type Mapping\n* The SeCo dataset contains image patches from Sentinel-2 tiles captured at different timestamps at each geographical location. [Download SeCo here](https://github.com/ElementAI/seasonal-contrast)\n* [SYSU-CD](https://github.com/liumency/SYSU-CD) -\u003e The dataset contains 20000 pairs of 0.5-m aerial images of size 256×256 taken between the years 2007 and 2014 in Hong Kong\n\n### DEM (digital elevation maps)\n* Shuttle Radar Topography Mission, search online at usgs.gov\n* Copernicus Digital Elevation Model (DEM) on S3, represents the surface of the Earth including buildings, infrastructure and vegetation. Data is provided as Cloud Optimized GeoTIFFs. [link](https://registry.opendata.aws/copernicus-dem/)\n* [Awesome-DEM](https://github.com/DahnJ/Awesome-DEM)\n\n## UAV \u0026 Drone datasets\n* Many on https://www.visualdata.io\n* [AU-AIR dataset](https://bozcani.github.io/auairdataset) -\u003e a multi-modal UAV dataset for object detection.\n* [ERA](https://lcmou.github.io/ERA_Dataset/) -\u003e  A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos.\n* [Aerial Maritime Drone Dataset](https://public.roboflow.ai/object-detection/aerial-maritime) -\u003e bounding boxes\n* [RetinaNet for pedestrian detection](https://towardsdatascience.com/pedestrian-detection-in-aerial-images-using-retinanet-9053e8a72c6) -\u003e bounding boxes\n* [BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos](https://github.com/exb7900/BIRDSAI) -\u003e Thermal IR videos of humans and animals\n* [ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos](https://lcmou.github.io/ERA_Dataset/)\n* [DroneVehicle](https://github.com/VisDrone/DroneVehicle) -\u003e Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning. Annotations are rotated bounding boxes. With [Github repo](https://github.com/SunYM2020/UA-CMDet)\n* [UAVOD10](https://github.com/weihancug/10-category-UAV-small-weak-object-detection-dataset-UAVOD10) -\u003e 10 class of objects at 15 cm resolution. Classes are; building, ship, vehicle, prefabricated house, well, cable tower, pool, landslide, cultivation mesh cage, and quarry. Bounding boxes\n* [Busy-parking-lot-dataset---vehicle-detection-in-UAV-video](https://github.com/zhu-xlab/Busy-parking-lot-dataset---vehicle-detection-in-UAV-video) -\u003e Vehicle instance segmentation. Unsure format of annotations, possible Matlab specific\n* [dd-ml-segmentation-benchmark](https://github.com/dronedeploy/dd-ml-segmentation-benchmark) -\u003e DroneDeploy Machine Learning Segmentation Benchmark\n* [SeaDronesSee](https://github.com/Ben93kie/SeaDronesSee) -\u003e Vision Benchmark for Maritime Search and Rescue. Bounding box object detection, single-object tracking and multi-object tracking annotations\n* [aeroscapes](https://github.com/ishann/aeroscapes) -\u003e semantic segmentation benchmark comprises of images captured using a commercial drone from an altitude range of 5 to 50 metres.\n* [ALTO](https://github.com/MetaSLAM/ALTO) -\u003e Aerial-view Large-scale Terrain-Oriented. For deep learning based UAV visual place recognition and localization tasks.\n* [HIT-UAV-Infrared-Thermal-Dataset](https://github.com/suojiashun/HIT-UAV-Infrared-Thermal-Dataset) -\u003e A High-altitude Infrared Thermal Object Detection Dataset for Unmanned Aerial Vehicles\n* [caltech-aerial-rgbt-dataset](https://github.com/aerorobotics/caltech-aerial-rgbt-dataset) -\u003e synchronized RGB, thermal, GPS, and IMU data\n* [Leafy Spurge Dataset](https://leafy-spurge-dataset.github.io/) -\u003e Real-world Weed Classification Within Aerial Drone Imagery\n* [UAV-HSI-Crop-Dataset](https://github.com/MrSuperNiu/UAV-HSI-Crop-Dataset) -\u003e dataset for \"HSI-TransUNet: A Transformer based semantic segmentation model for crop mapping from UAV hyperspectral imagery\"\n* [UAVVaste](https://github.com/PUTvision/UAVVaste) -\u003e COCO-like dataset and effective waste detection in aerial images\n\n## Other datasets\n\n### Object Detection \u0026 Classification\n* [RSOD-Dataset](https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-) -\u003e dataset for object detection in PASCAL VOC format. Aircraft, playgrounds, overpasses \u0026 oiltanks\n* [VHR-10_dataset_coco](https://github.com/chaozhong2010/VHR-10_dataset_coco) -\u003e Object detection and instance segmentation dataset based on NWPU VHR-10 dataset. RGB \u0026 SAR\n* [MAR20](https://gcheng-nwpu.github.io/) -\u003e Military Aircraft Recognition dataset\n* [Sewage-Treatment-Plant-Dataset](https://github.com/peijinwang/Sewage-Treatment-Plant-Dataset) -\u003e object detection\n* [TGRS-HRRSD-Dataset](https://github.com/CrazyStoneonRoad/TGRS-HRRSD-Dataset) -\u003e High Resolution Remote Sensing Detection (HRRSD)\n* [OGST](https://data.mendeley.com/datasets/bkxj8z84m9/3) -\u003e Oil and Gas Tank Dataset\n* [SearchAndRescueNet](https://github.com/michaelthoreau/SearchAndRescueNet) -\u003e Satellite Imagery for Search And Rescue Dataset, with example Faster R-CNN model\n* [UBC-dataset](https://github.com/AICyberTeam/UBC-dataset) -\u003e a dataset for building detection and classification from very high-resolution satellite imagery with the focus on object-level interpretation of individual buildings\n* [Building_Dataset](https://github.com/QiaoWenfan/Building_Dataset) -\u003e High-speed Rail Line Building Dataset Display\n* [RID](https://github.com/TUMFTM/RID) -\u003e Roof Information Dataset for CV-Based Photovoltaic Potential Assessment. With [paper](https://www.mdpi.com/2072-4292/14/10/2299)\n* [APKLOT](https://github.com/langheran/APKLOT) -\u003e A dataset for aerial parking block segmentation\n* [SAR-ACD](https://github.com/AICyberTeam/SAR-ACD) -\u003e SAR-ACD consists of 4322 aircraft clips with 6 civil aircraft categories and 14 other aircraft categories\n* [SODA](https://shaunyuan22.github.io/SODA/) -\u003e A large-scale Small Object Detection dataset. SODA-A comprises 2510 high-resolution images of aerial scenes, which has 800203 instances annotated with oriented rectangle box annotations over 9 classes.\n* [urban-tree-detection-data](https://github.com/jonathanventura/urban-tree-detection-data) -\u003e Dataset for training and evaluating tree detectors in urban environments with aerial imagery\n* [Satellite imagery datasets containing ships](https://github.com/NaLiu613/Satellite-Imagery-Datasets-Containing-Ships) -\u003e A list of radar and optical satellite datasets for ship detection, classification, semantic segmentation and instance segmentation tasks\n* [Roofline-Extraction](https://github.com/loosgagnet/Roofline-Extraction) -\u003e dataset for paper 'Knowledge-Based 3D Building Reconstruction (3DBR) Using Single Aerial Images and Convolutional Neural Networks (CNNs)'\n* [Building-detection-and-roof-type-recognition](https://github.com/loosgagnet/Building-detection-and-roof-type-recognition) -\u003e datasets for the paper 'A CNN-Based Approach for Automatic Building Detection and Recognition of Roof Types Using a Single Aerial Image'\n* [OnlyPlanes](https://github.com/naivelogic/OnlyPlanes) -\u003e Synthetic dataset and pretrained models for Detectron2\n* [SV248S](https://github.com/xdai-dlgvv/SV248S) -\u003e Single Object Tracking Dataset, tracking Vehicle, Large-Vehicle, Ship and Airplane\n* [NWPU-MOC](https://github.com/lyongo/NWPU-MOC) -\u003e A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images\n* [Vehicle Perception from Satellite](https://github.com/Chenxi1510/Vehicle-Perception-from-Satellite-Videos) -\u003e a large-scale benchmark for traffic monitoring from satellite\n* [SARDet-100K](https://github.com/zcablii/SARDet_100K) -\u003e Large-Scale Synthetic Aperture Radar (SAR) Object Detection\n* [Urban Vehicle Segmentation Dataset (UV6K)](https://zenodo.org/records/8404754)\n* [ShipRSImageNet](https://github.com/zzndream/ShipRSImageNet) -\u003e A Large-scale Fine-Grained Dataset for Ship Detection in High-Resolution Optical Remote Sensing Images\n* [VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond](https://github.com/nalemadi/VME_CDSI_dataset_benchmark)\n* [VHRV: Very High-Resolution Benchmark Dataset for Vessel Detection](https://github.com/buyukkanber/vhrv)\n\n### Land Use \u0026 Land Cover\n* [land-use-land-cover-datasets](https://github.com/r-wenger/land-use-land-cover-datasets)\n* [RSD46-WHU](https://github.com/RSIA-LIESMARS-WHU/RSD46-WHU) -\u003e 46 scene classes for image classification, free for education, research and commercial use\n* [RSSCN7](https://github.com/palewithout/RSSCN7) -\u003e Dataset of the article \"Deep Learning Based Feature Selection for Remote Sensing Scene Classification\"\n* [geonrw](https://ieee-dataport.org/open-access/geonrw) -\u003e orthorectified aerial photographs, LiDAR derived digital elevation models and segmentation maps with 10 classes. With [repo](https://github.com/gbaier/geonrw)\n* [Attribute-Cooperated-Classification-Datasets](https://github.com/CrazyStoneonRoad/Attribute-Cooperated-Classification-Datasets) -\u003e Three datasets based on AID, UCM, and Sydney. For each image, there is a label of scene classification and a label vector of attribute items.\n* [open_earth_map](https://github.com/bao18/open_earth_map) -\u003e a benchmark dataset for global high-resolution land cover mapping\n* [Mumbai-Semantic-Segmentation-Dataset](https://github.com/GeoAI-Research-Lab/Mumbai-Semantic-Segmentation-Dataset)\n* [GAMUS](https://github.com/EarthNets/RSI-MMSegmentation) -\u003e  A Geometry-aware Multi-modal Semantic Segmentation Benchmark for Remote Sensing Data\n* [openWUSU](https://github.com/AngieNikki/openWUSU) -\u003e WUSU is a semantic understanding dataset focusing on urban structure and the urbanization process in Wuhan\n* [RSE_Cross-city](https://github.com/danfenghong/RSE_Cross-city) -\u003e Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks\n* [AErial Lane](https://github.com/Jiawei-Yao0812/AerialLaneNet) -\u003e AErial Lane (AEL) Dataset is a first large-scale aerial image dataset built for lane detection, with high-quality polyline lane annotations on high-resolution images of around 80 kilometers of road\n* [Chesapeake Roads Spatial Context (RSC)](https://github.com/isaaccorley/ChesapeakeRSC)\n* [So2Sat-POP-DL](https://github.com/zhu-xlab/So2Sat-POP-DL) -\u003e Dataset discovery: So2Sat Population dataset covering 98 EU cities\n* [HouseTS](https://www.kaggle.com/datasets/shengkunwang/housets-dataset) -\u003e Long-term, Multimodal Housing Dataset Across 30 U.S. Metropolitan Area. Uses NAIP. [With paper](https://arxiv.org/abs/2506.00765)\n* [10,000 Crop Field Boundaries across India](https://zenodo.org/records/7315090) -\u003e using Airbus SPOT\n* [OpenEarthMap-SAR](https://github.com/cliffbb/OpenEarthMap-SAR) -\u003e VHR SAR used in the 2025 IEEE GRSS Data Fusion Contest Track 1: All-Weather Land Cover Mapping. Utilises data from Umbra and Capella Space\n* [Tokyo Land Use Land Cover Dataset](https://github.com/Tusaifei/Tokyo_dataset) -\u003e  0.5-m resolution images, two kinds of 10-m resolution LCPs, and two kinds of 30-m resolution LCPs\n\n### Change Detection\n* [S2Looking](https://github.com/S2Looking/Dataset) -\u003e A Satellite Side-Looking Dataset for Building Change Detection, [paper](https://arxiv.org/abs/2107.09244)\n* [Haiming-Z/MtS-WH-reference-map](https://github.com/Haiming-Z/MtS-WH-reference-map) -\u003e a reference map for change detection based on MtS-WH\n* [MtS-WH-Dataset](https://github.com/rulixiang/MtS-WH-Dataset) -\u003e Multi-temporal Scene WuHan (MtS-WH) Dataset\n* [SZTAKI](http://web.eee.sztaki.hu/remotesensing/airchange_benchmark.html) -\u003e A Ground truth collection for change detection in optical aerial images taken with several years time differences\n* [DSIFN](https://github.com/GeoZcx/A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images/tree/master/dataset) -\u003e change detection dataset, it consists of six large bi-temporal high resolution images covering six cities in China\n* [Road-Change-Detection-Dataset](https://github.com/fightingMinty/Road-Change-Detection-Dataset)\n* [3DCD](https://sites.google.com/uniroma1.it/3dchangedetection/home-page) -\u003e infer 3D CD maps using only remote sensing optical bitemporal images as input without the need of Digital Elevation Models (DEMs)\n* [TUE-CD](https://github.com/RSMagneto/MSI-Net) -\u003e A change detection detection for building damage estimation after earthquake\n* [Hi-UCD](https://github.com/Daisy-7/Hi-UCD-S) -\u003e ultra-High Urban Change Detection for urban semantic change detection\n* [LEVIR-CC-Dataset](https://github.com/Chen-Yang-Liu/LEVIR-CC-Dataset) -\u003e A Large Dataset for Remote Sensing Image Change Captioning\n* [GDCLD](https://zenodo.org/records/13612636) -\u003e A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images\n* [BANet change dataset - RS image to cadastral map](https://github.com/lqycrystal/BANet)\n* [Indian Cities Change Detection (ICCD) Dataset](https://ieee-dataport.org/documents/indian-cities-change-detection-iccd-dataset)\n\n### SAR-Specific Datasets\n* [HRSID](https://github.com/chaozhong2010/HRSID) -\u003e high resolution sar images dataset for ship detection, semantic segmentation, and instance segmentation tasks\n* [LS-SSDD-v1.0-OPEN](https://github.com/TianwenZhang0825/LS-SSDD-v1.0-OPEN) -\u003e Large-Scale SAR Ship Detection Dataset\n* [WHU-SEN-City](https://github.com/whu-csl/WHU-SEN-City) -\u003e A paired SAR-to-optical image translation dataset which covers 34 big cities of China\n* [SAR_vehicle_detection_dataset](https://github.com/whu-csl/SAR_vehicle_detection_dataset) -\u003e 104 SAR images for vehicle detection, collected from Sandia MiniSAR/FARAD SAR images and MSTAR images\n* [AIR-PolSAR-Seg](https://github.com/AICyberTeam/AIR-PolSAR-Seg) -\u003e a challenging PolSAR terrain segmentation dataset\n* [QXS-SAROPT](https://github.com/yaoxu008/QXS-SAROPT) -\u003e Optical and SAR pairing dataset from the [paper](https://arxiv.org/abs/2103.08259): The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion\n* [SynthWakeSAR](https://data.bris.ac.uk/data/dataset/30kvuvmatwzij2mz1573zqumfx) -\u003e A Synthetic SAR Dataset for Deep Learning Classification of Ships at Sea, with [paper](https://www.mdpi.com/2072-4292/14/16/3999)\n* [SAR2Opt-Heterogeneous-Dataset](https://github.com/MarsZhaoYT/SAR2Opt-Heterogeneous-Dataset) -\u003e SAR-optical images to be used as a benchmark in change detection and image transaltion on remote sensing images\n* [OpenSARWake](https://github.com/libzzluo/OpenSARWake) -\u003e A SAR ship wake rotation detection benchmark dataset.\n\n### Specialized Applications\n* [MUSIC4HA](https://github.com/gistairc/MUSIC4HA) -\u003e MUltiband Satellite Imagery for object Classification (MUSIC) to detect Hot Area\n* [MUSIC4GC](https://github.com/gistairc/MUSIC4GC) -\u003e MUltiband Satellite Imagery for object Classification (MUSIC) to detect Golf Course\n* [MUSIC4P3](https://github.com/gistairc/MUSIC4P3) -\u003e MUltiband Satellite Imagery for object Classification (MUSIC) to detect Photovoltaic Power Plants (solar panels)\n* [ABCDdataset](https://github.com/gistairc/ABCDdataset) -\u003e damage detection dataset to identify whether buildings have been washed-away by tsunami\n* [Thermal power plans dataset](https://github.com/wenxinYin/AIR-TPPDD)\n* [SolarDK](https://arxiv.org/abs/2212.01260) -\u003e A high-resolution urban solar panel image classification and localization dataset\n* [Oil and Gas Infrastructure Mapping (OGIM) database](https://zenodo.org/record/7922117) -\u003e includes locations and facility attributes of oil and gas infrastructure types that are important sources of methane emissions\n* [Overhead Wind Turbine Dataset - NAIP](https://zenodo.org/records/7385227#.Y419qezMLdr)\n* [CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds](https://zenodo.org/records/10042922) -\u003e the dataset consists of 1,780 MODIS satellite images hand-labeled for the presence of more than 12,000 ship tracks.\n* [Digital Typhoon Dataset](https://github.com/kitamoto-lab/digital-typhoon/) -\u003e aimed at benchmarking machine learning models for long-term spatio-temporal data\n* [BirdSAT](https://github.com/mvrl/BirdSAT) -\u003e Cross-View iNAT Birds 2021: This cross-view birds species dataset consists of paired ground-level bird images and satellite images, along with meta-information associated with the iNaturalist-2021 dataset.\n* [RSHaze+](https://zenodo.org/records/13837162) -\u003e remote sensing dehazing datasets in PhDnet: A novel physic-aware dehazing network for remote sensing images\n* [GMSEUS](https://github.com/stidjaco/GMSEUS) -\u003e A comprehensive ground-mounted solar energy dataset with sub-array design metadata in the United States\n* [MultiviewRS](https://github.com/fmenat/multiviewRS-datasets) -\u003e List of remote sensing (RS) multi-view datasets for exploring multi-view learning\n* [SatDepth](https://satdepth.pythonanywhere.com/) -\u003e A Novel Dataset for Satellite Image Matching and Depth Estimation\n* [OpenSatMap](https://huggingface.co/datasets/z-hb/OpenSatMap) -\u003e for large-scale map construction and downstream tasks like autonomous driving\n\n### Agricultural \u0026 Environmental\n* [Hyperspectral Change Detection Dataset Irrigated Agricultural Area](https://github.com/SicongLiuRS/Hyperspectral-Change-Detection-Dataset-Irrigated-Agricultural-Area)\n* [CNN-RNN-Yield-Prediction](https://github.com/saeedkhaki92/CNN-RNN-Yield-Prediction) -\u003e soybean dataset\n* [FireRisk](https://github.com/CharmonyShen/FireRisk) -\u003e A Remote Sensing Dataset for Fire Risk Assessment with Benchmarks Using Supervised and Self-supervised Learning\n* [TimeMatch](https://zenodo.org/records/5636422) -\u003e dataset for cross-region adaptation for crop identification from SITS in four different regions in Europe\n* [Landsat 8 Cloud Cover Assessment Validation Data](https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data)\n* [Remote Sensing Satellite Video Dataset for Super-resolution](https://zenodo.org/record/6969604#.ZCBd-OzMJhE)\n* [SpatioTemporalYield](https://huggingface.co/datasets/ellaampy/SpatioTemporalYield) -\u003e covers the USA’s top five corn-producing states: Iowa, Illinois, Indiana, Nebraska, and Minnesota.\n* [Palm Tree Dataset](https://github.com/NourO93/Palm-Tree-Dataset/tree/main)\n* [ts-satfire](https://www.kaggle.com/datasets/z789456sx/ts-satfire) -\u003e A Multi-Task Satellite Image Time-Series Dataset for Wildfire Detection and Prediction\n* [GTPBD](https://github.com/Z-ZW-WXQ/GTPBD/) -\u003e Global Terraced Parcel and Boundary Dataset\n\n### Hyperspectral \u0026 Multi-modal\n* [AeroRIT](https://github.com/aneesh3108/AeroRIT) -\u003e A New Scene for Hyperspectral Image Analysis\n* [Data-CSHSI](https://github.com/YuxiangZhang-BIT/Data-CSHSI) -\u003e Open source datasets for Cross-Scene Hyperspectral Image Classification, includes Houston, Pavia \u0026 HyRank datasets\n* [HySpecNet-11k](https://hyspecnet.rsim.berlin/) -\u003e a large-scale hyperspectral benchmark dataset\n* [STARCOP dataset: Semantic Segmentation of Methane Plumes with Hyperspectral Machine Learning Models](https://zenodo.org/records/7863343)\n* [Toulouse Hyperspectral Data Set](https://www.toulouse-hyperspectral-data-set.com/)\n* [Toulouse Hyperspectral Data Set](https://github.com/Romain3Ch216/TlseHypDataSet)\n* [Multi-modality-image-matching](https://github.com/StaRainJ/Multi-modality-image-matching-database-metrics-methods) -\u003e image matching dataset including several remote sensing modalities\n* [PanCollection](https://github.com/liangjiandeng/PanCollection) -\u003e Pansharpening Datasets from WorldView 2, WorldView 3, QuickBird, Gaofen 2 sensors\n\n### Benchmark \u0026 Foundation Models\n* [EORSSD-dataset](https://github.com/rmcong/EORSSD-dataset) -\u003e Extended Optical Remote Sensing Saliency Detection (EORSSD) Dataset\n* [ERA-DATASET](https://github.com/zhu-xlab/ERA-DATASET) -\u003e A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos\n* [SSL4EO-S12](https://github.com/zhu-xlab/SSL4EO-S12) -\u003e a large-scale dataset for self-supervised learning in Earth observation\n* [AIR-CD](https://github.com/AICyberTeam/AIR-CD) -\u003e a challenging cloud detection data set called AIR-CD, with higher spatial resolution and more representative landcover types\n* [HRC_WHU](https://github.com/dr-lizhiwei/HRC_WHU) -\u003e High-Resolution Cloud Detection Dataset comprising 150 RGB images and a resolution varying from 0.5 to 15 m in different global regions\n* [University1652-Baseline](https://github.com/layumi/University1652-Baseline) -\u003e A Multi-view Multi-source Benchmark for Drone-based Geo-localization\n* [benchmark_ISPRS2021](https://github.com/whuwuteng/benchmark_ISPRS2021) -\u003e A new stereo dense matching benchmark dataset for deep learning\n* [WHU-Stereo](https://github.com/Sheng029/WHU-Stereo) -\u003e A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images\n* [GeoPile pretraining dataset](https://github.com/mmendiet/GFM) -\u003e compiles imagery from other datasets including RSD46-WHU, MLRSNet and RESISC45 for pretraining of Foundational models\n* [pangaea-bench](https://github.com/yurujaja/pangaea-bench) -\u003e A Global and Inclusive Benchmark for Geospatial Foundation Models\n* [VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding](https://vrsbench.github.io/)\n* [SeeFar](https://coastalcarbon.ai/seefar) -\u003e Satellite Agnostic Multi-Resolution Dataset for Geospatial Foundation Models\n* [dynnet](https://github.com/aysim/dynnet) -\u003e DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation\n* [Awesome-Remote-Sensing-Relative-Radiometric-Normalization-Datasets](https://github.com/ArminMoghimi/Awesome-Remote-Sensing-Relative-Radiometric-Normalization-Datasets)\n* [AISD](https://github.com/RSrscoder/AISD) -\u003e Aerial Imagery dataset for Shadow Detection## Kaggle\nKaggle hosts over \u003e 200 satellite image datasets, [search results here](https://www.kaggle.com/search?q=satellite+image+in%3Adatasets).\nThe [kaggle blog](http://blog.kaggle.com) is an interesting read.\n\n### Kaggle - Amazon from space - classification challenge\n* https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/data\n* 3-5 meter resolution GeoTIFF images from planet Dove satellite constellation\n* 12 classes including - **cloudy, primary + waterway** etc\n* [1st place winner interview - used 11 custom CNN](http://blog.kaggle.com/2017/10/17/planet-understanding-the-amazon-from-space-1st-place-winners-interview/)\n* [FastAI Multi-label image classification](https://towardsdatascience.com/fastai-multi-label-image-classification-8034be646e95)\n* [Multi-Label Classification of Satellite Photos of the Amazon Rainforest](https://machinelearningmastery.com/how-to-develop-a-convolutional-neural-network-to-classify-satellite-photos-of-the-amazon-rainforest/)\n* [Understanding the Amazon Rainforest with Multi-Label Classification + VGG-19, Inceptionv3, AlexNet \u0026 Transfer Learning](https://towardsdatascience.com/understanding-the-amazon-rainforest-with-multi-label-classification-vgg-19-inceptionv3-5084544fb655)\n* [amazon-classifier](https://github.com/mikeskaug/amazon-classifier) -\u003e compares random forest with CNN\n* [multilabel-classification](https://github.com/muneeb706/multilabel-classification) -\u003e compares various CNN architecutres\n* [Planet-Amazon-Kaggle](https://github.com/Skumarr53/Planet-Amazon-Kaggle) -\u003e uses fast.ai\n* [deforestation_deep_learning](https://github.com/schumanzhang/deforestation_deep_learning)\n* [Track-Human-Footprint-in-Amazon-using-Deep-Learning](https://github.com/sahanasub/Track-Human-Footprint-in-Amazon-using-Deep-Learning)\n* [Amazon-Rainforest-CNN](https://github.com/cldowdy/Amazon-Rainforest-CNN) -\u003e uses a 3-layer CNN in Tensorflow\n* [rainforest-tagging](https://github.com/minggli/rainforest-tagging) -\u003e Convolutional Neural Net and Recurrent Neural Net in Tensorflow for satellite images multi-label classification\n* [satellite-deforestation](https://github.com/drewhibbard/satellite-deforestation) -\u003e Using Satellite Imagery to Identify the Leading Indicators of Deforestation, applied to the Kaggle Challenge Understanding the Amazon from Space\n\n### Kaggle - DSTL segmentation challenge\n* https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection\n* Rating - medium, many good examples (see the Discussion as well as kernels), but as this competition was run a couple of years ago many examples use python 2\n* WorldView 3 - 45 satellite images covering 1km x 1km in both 3 (i.e. RGB) and 16-band (400nm - SWIR) images\n* 10 Labelled classes include - **Buildings, Road, Trees, Crops, Waterway, Vehicles**\n* [Interview with 1st place winner who used segmentation networks](http://blog.kaggle.com/2017/04/26/dstl-satellite-imagery-competition-1st-place-winners-interview-kyle-lee/) - 40+ models, each tweaked for particular target (e.g. roads, trees)\n* [ZF_UNET_224_Pretrained_Model 2nd place solution](https://github.com/ZFTurbo/ZF_UNET_224_Pretrained_Model) -\u003e\n* [3rd place soluton](https://github.com/osin-vladimir/kaggle-satellite-imagery-feature-detection) -\u003e which explored pansharpening \u0026 calculating reflectance indices, with [arxiv paper](https://arxiv.org/abs/1706.06169) \n* [Deepsense 4th place solution](https://deepsense.ai/deep-learning-for-satellite-imagery-via-image-segmentation/)\n* [Entry by lopuhin](https://github.com/lopuhin/kaggle-dstl) using UNet with batch-normalization\n* [Multi-class semantic segmentation of satellite images using U-Net](https://github.com/rogerxujiang/dstl_unet) using DSTL dataset, tensorflow 1 \u0026 python 2.7. Accompanying [article](https://towardsdatascience.com/dstl-satellite-imagery-contest-on-kaggle-2f3ef7b8ac40)\n* [Deep-Satellite-Image-Segmentation](https://github.com/antoine-spahr/Deep-Satellite-Image-Segmentation)\n* [Dstl-Satellite-Imagery-Feature-Detection-Improved](https://github.com/dsp6414/Dstl-Satellite-Imagery-Feature-Detection-Improved)\n* [Satellite-imagery-feature-detection](https://github.com/ArangurenAndres/Satellite-imagery-feature-detection)\n* [Satellite_Image_Classification](https://github.com/aditya-sawadh/Satellite_Image_Classification) -\u003e using XGBoost and ensemble classification methods\n* [Unet-for-Satellite](https://github.com/justinishikawa/Unet-for-Satellite)\n* [building-segmentation](https://github.com/jimpala/building-segmentation) -\u003e TensorFlow U-Net implementation trained to segment buildings in satellite imagery\n\n### Kaggle - DeepSat land cover classification\n* https://www.kaggle.com/datasets/crawford/deepsat-sat4 \u0026 https://www.kaggle.com/datasets/crawford/deepsat-sat6\n* [DeepSat-Kaggle](https://github.com/athulsudheesh/DeepSat-Kaggle) -\u003e uses Julia\n* [deepsat-aws-emr-pyspark](https://github.com/hellosaumil/deepsat-aws-emr-pyspark) -\u003e Using PySpark for Image Classification on Satellite Imagery of Agricultural Terrains\n\n### Kaggle - Airbus ship detection challenge\n* https://www.kaggle.com/c/airbus-ship-detection/overview\n* Rating - medium, most solutions using deep-learning, many kernels, [good example kernel](https://www.kaggle.com/kmader/baseline-u-net-model-part-1)\n* [Detecting ships in satellite imagery: five years later…](https://medium.com/artificialis/detecting-ships-in-satellite-imagery-five-years-later-28df2e83f987)\n* I believe there was a problem with this dataset, which led to many complaints that the competition was ruined\n* [Lessons Learned from Kaggle’s Airbus Challenge](https://towardsdatascience.com/lessons-learned-from-kaggles-airbus-challenge-252e25c5efac)\n* [Airbus-Ship-Detection](https://github.com/kheyer/Airbus-Ship-Detection) -\u003e This solution scored 139 out of 884 for the competition, combines ResNeXt50 based classifier and a U-net segmentation model\n* [Ship-Detection-Project](https://github.com/ZTong1201/Ship-Detection-Project) -\u003e uses Mask R-CNN and UNet model\n* [Airbus_SDC](https://github.com/WillieMaddox/Airbus_SDC)\n* [Airbus_SDC_dup](https://github.com/WillieMaddox/Airbus_SDC_dup) -\u003e Project focused on detecting duplicate regions of overlapping satellite imagery. Applied to Airbus ship detection dataset\n* [airbus-ship-detection](https://github.com/jancervenka/airbus-ship-detection) -\u003e CNN with REST API\n* [Ship-Detection-from-Satellite-Images-using-YOLOV4](https://github.com/debasis-dotcom/Ship-Detection-from-Satellite-Images-using-YOLOV4) -\u003e uses Kaggle Airbus Ship Detection dataset\n* [Image Segmentation: Kaggle experience](https://towardsdatascience.com/image-segmentation-kaggle-experience-9a41cb8924f0) -\u003e Medium article by gold medal winner Vlad Shmyhlo\n\n### Kaggle - Ships in Google Earth\n* https://www.kaggle.com/datasets/tomluther/ships-in-google-earth\n* 794 jpegs showing various sized ships in satellite imagery, annotations in Pascal VOC format for object detection models\n* [/kaggle-ships-in-satellite-imagery-with-YOLOv8](https://github.com/robmarkcole/kaggle-ships-in-satellite-imagery-with-YOLOv8)\n\n### Kaggle - Classify Ships in San Franciso Bay using Planet satellite imagery\n* https://www.kaggle.com/datasets/rhammell/ships-in-satellite-imagery\n* 4000 80x80 RGB images labeled with either a \"ship\" or \"no-ship\" classification, 3 meter pixel size\n* [shipsnet-detector](https://github.com/rhammell/shipsnet-detector) -\u003e Detect container ships in Planet imagery using machine learning\n* [DeepLearningShipDetection](https://github.com/PenguinDan/DeepLearningShipDetection)\n* [Ship-Detection-Using-Satellite-Imagery](https://github.com/Dhruvisha29/Ship-Detection-Using-Satellite-Imagery)\n\n### Kaggle - Planesnet classification dataset\n* https://www.kaggle.com/datasets/rhammell/planesnet -\u003e Detect aircraft in Planet satellite image chips\n* 20x20 RGB images, the \"plane\" class includes 8000 images and the \"no-plane\" class includes 24000 images\n* [Dataset repo](https://github.com/rhammell/planesnet) and [planesnet-detector](https://github.com/rhammell/planesnet-detector) demonstrates a small CNN classifier on this dataset\n* [ergo-planes-detector](https://github.com/evilsocket/ergo-planes-detector) -\u003e An ergo based project that relies on a convolutional neural network to detect airplanes from satellite imagery, uses the PlanesNet dataset\n* [Using AWS SageMaker/PlanesNet to process Satellite Imagery](https://github.com/kskalvar/aws-sagemaker-planesnet-imagery)\n* [Airplane-in-Planet-Image](https://github.com/MaxLenormand/Airplane-in-Planet-Image) -\u003e pytorch model\n\n### Kaggle - CGI Planes in Satellite Imagery w/ BBoxes\n* https://www.kaggle.com/datasets/aceofspades914/cgi-planes-in-satellite-imagery-w-bboxes\n* 500 computer generated satellite images of planes\n* [Faster RCNN to detect airplanes](https://github.com/ShubhankarRawat/Airplane-Detection-for-Satellites)\n* [aircraft-detection-from-satellite-images-yolov3](https://github.com/emrekrtorun/aircraft-detection-from-satellite-images-yolov3)\n\n### Kaggle - Swimming pool and car detection using satellite imagery\n* https://www.kaggle.com/datasets/kbhartiya83/swimming-pool-and-car-detection\n* 3750 satellite images of residential areas with annotation data for swimming pools and cars\n* [Object detection on Satellite Imagery using RetinaNet](https://medium.com/@ije_good/object-detection-on-satellite-imagery-using-retinanet-part-1-training-e589975afbd5)\n\n### Kaggle - Draper challenge to place images in order of time\n* https://www.kaggle.com/c/draper-satellite-image-chronology/data\n* Rating - hard. Not many useful kernels.\n* Images are grouped into sets of five, each of which have the same setId. Each image in a set was taken on a different day (but not necessarily at the same time each day). The images for each set cover approximately the same area but are not exactly aligned.\n* Kaggle interviews for entrants who [used XGBOOST](http://blog.kaggle.com/2016/09/15/draper-satellite-image-chronology-machine-learning-solution-vicens-gaitan/) and a [hybrid human/ML approach](http://blog.kaggle.com/2016/09/08/draper-satellite-image-chronology-damien-soukhavong/)\n* [deep-cnn-sat-image-time-series](https://github.com/MickyDowns/deep-cnn-sat-image-time-series) -\u003e uses LSTM\n\n### Kaggle - Dubai segmentation\n* https://www.kaggle.com/datasets/humansintheloop/semantic-segmentation-of-aerial-imagery\n* 72 satellite images of Dubai, the UAE, and is segmented into 6 classes\n* [dubai-satellite-imagery-segmentation](https://github.com/ayushdabra/dubai-satellite-imagery-segmentation) -\u003e due to the small dataset, image augmentation was used\n* [U-Net for Semantic Segmentation on Unbalanced Aerial Imagery](https://towardsdatascience.com/u-net-for-semantic-segmentation-on-unbalanced-aerial-imagery-3474fa1d3e56) -\u003e using the Dubai dataset\n* [Semantic-Segmentation-using-U-Net](https://github.com/Anay21110/Semantic-Segmentation-using-U-Net) -\u003e uses keras\n* [unet_satelite_image_segmentation](https://github.com/nassimaliou/unet_satelite_image_segmentation)\n\n### Kaggle - Massachusetts Roads \u0026 Buildings Datasets - segmentation\n* https://www.kaggle.com/datasets/balraj98/massachusetts-roads-dataset\n* https://www.kaggle.com/datasets/balraj98/massachusetts-buildings-dataset\n* [Official published dataset](https://www.cs.toronto.edu/~vmnih/data/)\n* [Road_seg_dataset](https://github.com/parth1620/Road_seg_dataset) -\u003e subset of the roads dataset containing only 200 images and masks\n* [Road and Building Semantic Segmentation in Satellite Imagery](https://github.com/Paulymorphous/Road-Segmentation) uses U-Net on the Massachusetts Roads Dataset \u0026 keras\n* [Semantic-segmentation repo by fuweifu-vtoo](https://github.com/fuweifu-vtoo/Semantic-segmentation) -\u003e uses pytorch and the [Massachusetts Buildings \u0026 Roads Datasets](https://www.cs.toronto.edu/~vmnih/data/)\n* [ssai-cnn](https://github.com/mitmul/ssai-cnn) -\u003e This is an implementation of Volodymyr Mnih's dissertation methods on his Massachusetts road \u0026 building dataset\n* [building-footprint-segmentation](https://github.com/fuzailpalnak/building-footprint-segmentation) -\u003e pip installable library to train building footprint segmentation on satellite and aerial imagery, applied to Massachusetts Buildings Dataset and Inria Aerial Image Labeling Dataset\n* [Road detection using semantic segmentation and albumentations for data augmention](https://towardsdatascience.com/road-detection-using-segmentation-models-and-albumentations-libraries-on-keras-d5434eaf73a8) using the Massachusetts Roads Dataset, U-net \u0026 Keras\n* [Image-Segmentation)](https://github.com/mschulz/Image-Segmentation) -\u003e using Massachusetts Road dataset and fast.ai\n\n### Kaggle - Deepsat classification challenge\nNot satellite but airborne imagery. Each sample image is 28x28 pixels and consists of 4 bands - red, green, blue and near infrared. The training and test labels are one-hot encoded 1x6 vectors. Each image patch is size normalized to 28x28 pixels. Data in `.mat` Matlab format. JPEG?\n* [Sat4](https://www.kaggle.com/datasets/crawford/deepsat-sat4) 500,000 image patches covering four broad land cover classes - **barren land, trees, grassland and a class that consists of all land cover classes other than the above three**\n* [Sat6](https://www.kaggle.com/datasets/crawford/deepsat-sat6) 405,000 image patches each of size 28x28 and covering 6 landcover classes - **barren land, trees, grassland, roads, buildings and water bodies.**\n\n### Kaggle - High resolution ship collections 2016 (HRSC2016)\n* https://www.kaggle.com/datasets/guofeng/hrsc2016\n* Ship images harvested from Google Earth\n* [HRSC2016_SOTA](https://github.com/ming71/HRSC2016_SOTA) -\u003e Fair comparison of different algorithms on the HRSC2016 dataset\n\n### Kaggle - SWIM-Ship Wake Imagery Mass\n* https://www.kaggle.com/datasets/lilitopia/swimship-wake-imagery-mass\n* An optical ship wake detection benchmark dataset built for deep learning\n* [WakeNet](https://github.com/Lilytopia/WakeNet) -\u003e A CNN-based optical image ship wake detector, code for 2021 paper: Rethinking Automatic Ship Wake Detection: State-of-the-Art CNN-based Wake Detection via Optical Images\n\n### Kaggle - Understanding Clouds from Satellite Images\nIn this challenge, you will build a model to classify cloud organization patterns from satellite images.\n* https://www.kaggle.com/c/understanding_cloud_organization/\n* [3rd place solution on Github by naivelamb](https://github.com/naivelamb/kaggle-cloud-organization)\n* [15th place solution on Github by Soongja](https://github.com/Soongja/kaggle-clouds)\n* [69th place solution on Github by yukkyo](https://github.com/yukkyo/Kaggle-Understanding-Clouds-69th-solution)\n* [161st place solution on Github by michal-nahlik](https://github.com/michal-nahlik/kaggle-clouds-2019)\n* [Solution by yurayli](https://github.com/yurayli/satellite-cloud-segmentation)\n* [Solution by HazelMartindale](https://github.com/HazelMartindale/kaggle_understanding_clouds_learning_project) uses 3 versions of U-net architecture\n* [Solution by khornlund](https://github.com/khornlund/understanding-cloud-organization)\n* [Solution by Diyago](https://github.com/Diyago/Understanding-Clouds-from-Satellite-Images)\n* [Solution by tanishqgautam](https://github.com/tanishqgautam/Multi-Label-Segmentation-With-FastAI)\n\n### Kaggle - 38-Cloud Cloud Segmentation\n* https://www.kaggle.com/datasets/sorour/38cloud-cloud-segmentation-in-satellite-images\n* Contains 38 Landsat 8 images and manually extracted pixel-level ground truths\n* [38-Cloud Github repository](https://github.com/SorourMo/38-Cloud-A-Cloud-Segmentation-Dataset) and follow up [95-Cloud](https://github.com/SorourMo/95-Cloud-An-Extension-to-38-Cloud-Dataset) dataset\n* [How to create a custom Dataset / Loader in PyTorch, from Scratch, for multi-band Satellite Images Dataset from Kaggle](https://medium.com/analytics-vidhya/how-to-create-a-custom-dataset-loader-in-pytorch-from-scratch-for-multi-band-satellite-images-c5924e908edf)\n* [Cloud-Net: A semantic segmentation CNN for cloud detection](https://github.com/SorourMo/Cloud-Net-A-semantic-segmentation-CNN-for-cloud-detection) -\u003e an end-to-end cloud detection algorithm for Landsat 8 imagery, trained on 38-Cloud Training Set\n* [Segmentation of Clouds in Satellite Images Using Deep Learning](https://medium.com/swlh/segmentation-of-clouds-in-satellite-images-using-deep-learning-a9f56e0aa83d) -\u003e semantic segmentation using a Unet on the Kaggle 38-Cloud dataset\n\n### Kaggle - Airbus Aircraft Detection Dataset\n* https://www.kaggle.com/airbusgeo/airbus-aircrafts-sample-dataset\n* One hundred civilian airports and over 3000 annotated commercial aircrafts\n* [detecting-aircrafts-on-airbus-pleiades-imagery-with-yolov5](https://medium.com/artificialis/detecting-aircrafts-on-airbus-pleiades-imagery-with-yolov5-5f3d464b75ad)\n* [pytorch-remote-sensing](https://github.com/miko7879/pytorch-remote-sensing) -\u003e Aircraft detection using the 'Airbus Aircraft Detection' dataset and Faster-RCNN with ResNet-50 backbone in pytorch\n\n### Kaggle - Airbus oil storage detection dataset\n* https://www.kaggle.com/airbusgeo/airbus-oil-storage-detection-dataset\n* [Oil-Storage Tank Instance Segmentation with Mask R-CNN](https://github.com/georgiosouzounis/instance-segmentation-mask-rcnn/blob/main/mask_rcnn_oiltanks_gpu.ipynb) with [accompanying article](https://medium.com/@georgios.ouzounis/oil-storage-tank-instance-segmentation-with-mask-r-cnn-77c94433045f)\n* [Oil Storage Detection on Airbus Imagery with YOLOX](https://medium.com/artificialis/oil-storage-detection-on-airbus-imagery-with-yolox-9e38eb6f7e62) -\u003e uses the Kaggle Airbus Oil Storage Detection dataset\n* [Oil-Storage-Tanks-Data-Preparation-YOLO-Format](https://github.com/shah0nawaz/Oil-Storage-Tanks-Data-Preparation-YOLO-Format)\n\n### Kaggle - Satellite images of hurricane damage\n* https://www.kaggle.com/datasets/kmader/satellite-images-of-hurricane-damage\n* https://github.com/dbuscombe-usgs/HurricaneHarvey_buildingdamage\n\n### Kaggle - Austin Zoning Satellite Images\n* https://www.kaggle.com/datasets/franchenstein/austin-zoning-satellite-images\n* classify a images of Austin into one of its zones, such as residential, industrial, etc. 3667 satellite images\n\n### Kaggle - Statoil/C-CORE Iceberg Classifier Challenge\nClassify the target in a SAR image chip as either a ship or an iceberg. The dataset for the competition included 5000 images extracted from multichannel SAR data collected by the Sentinel-1 satellite. Top entries used ensembles to boost prediction accuracy from about 92% to 97%.\n* https://www.kaggle.com/c/statoil-iceberg-classifier-challenge/data\n* [An interview with David Austin: 1st place winner](https://pyimagesearch.com/2018/03/26/interview-david-austin-1st-place-25000-kaggles-popular-competition/)\n* [radar-image-recognition](https://github.com/siarez/radar-image-recognition)\n* [Iceberg-Classification-Using-Deep-Learning](https://github.com/mankadronit/Iceberg-Classification-Using-Deep-Learning) -\u003e uses keras\n* [Deep-Learning-Project](https://github.com/singh-shakti94/Deep-Learning-Project) -\u003e uses keras\n* [iceberg-classifier-challenge solution by ShehabSunny](https://github.com/ShehabSunny/iceberg-classifier-challenge) -\u003e uses keras\n* [Analyzing Satellite Radar Imagery with Deep Learning](https://uk.mathworks.com/company/newsletters/articles/analyzing-satellite-radar-imagery-with-deep-learning.html) -\u003e by Matlab, uses ensemble with greedy search\n* [16th place solution](https://github.com/sergeyshilin/kaggle-statoil-iceberg-classifier-challenge)\n* [fastai solution](https://github.com/smarkochev/ds_notebooks/blob/master/Statoil_Kaggle_competition_google_colab_notebook.ipynb)\n\n### Kaggle - Land Cover Classification Dataset from DeepGlobe Challenge - segmentation\n* https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset\n* [Satellite Imagery Semantic Segmentation with CNN](https://joshting.medium.com/satellite-imagery-segmentation-with-convolutional-neural-networks-f9254de3b907) -\u003e 7 different segmentation classes, DeepGlobe Land Cover Classification Challenge dataset, with [repo](https://github.com/justjoshtings/satellite_image_segmentation)\n* [Land Cover Classification with U-Net](https://baratam-tarunkumar.medium.com/land-cover-classification-with-u-net-aa618ea64a1b) -\u003e Satellite Image Multi-Class Semantic Segmentation Task with PyTorch Implementation of U-Net, uses DeepGlobe Land Cover Segmentation dataset, with [code](https://github.com/TarunKumar1995-glitch/land_cover_classification_unet)\n* [DeepGlobe Land Cover Classification Challenge solution](https://github.com/GeneralLi95/deepglobe_land_cover_classification_with_deeplabv3plus)\n\n### Kaggle - Next Day Wildfire Spread\nA Data Set to Predict Wildfire Spreading from Remote-Sensing Data\n* https://www.kaggle.com/datasets/fantineh/next-day-wildfire-spread\n* https://arxiv.org/abs/2112.02447\n\n### Kaggle - Satellite Next Day Wildfire Spread\nInspired by the above dataset, using different data sources\n* https://www.kaggle.com/datasets/satellitevu/satellite-next-day-wildfire-spread\n* https://github.com/SatelliteVu/SatelliteVu-AWS-Disaster-Response-Hackathon\n\n## Kaggle - Spacenet 7 Multi-Temporal Urban Change Detection\n* https://www.kaggle.com/datasets/amerii/spacenet-7-multitemporal-urban-development\n* [SatFootprint](https://github.com/PriyanK7n/SatFootprint) -\u003e building segmentation on the Spacenet 7 dataset\n\n## Kaggle - Satellite Images to predict poverty in Africa\n* https://www.kaggle.com/datasets/sandeshbhat/satellite-images-to-predict-povertyafrica\n* Uses satellite imagery and nightlights data to predict poverty levels at a local level\n* [Predicting-Poverty](https://github.com/jmather625/predicting-poverty-replication) -\u003e Combining satellite imagery and machine learning to predict poverty, in PyTorch\n\n## Kaggle - NOAA Fisheries Steller Sea Lion Population Count\n* https://www.kaggle.com/competitions/noaa-fisheries-steller-sea-lion-population-count -\u003e count sea lions from aerial images\n* [Sealion-counting](https://github.com/babyformula/Sealion-counting)\n* [Sealion_Detection_Classification](https://github.com/yyc9268/Sealion_Detection_Classification)\n\n## Kaggle - Arctic Sea Ice Image Masking\n* https://www.kaggle.com/datasets/alexandersylvester/arctic-sea-ice-image-masking\n* [sea_ice_remote_sensing](https://github.com/sum1lim/sea_ice_remote_sensing)\n\n## Kaggle - Overhead-MNIST\n* A Benchmark Satellite Dataset as Drop-In Replacement for MNIST\n* https://www.kaggle.com/datasets/datamunge/overheadmnist -\u003e kaggle\n* https://arxiv.org/abs/2102.04266 -\u003e paper\n* https://github.com/reveondivad/ov-mnist -\u003e github\n\n## Kaggle - Satellite Image Classification\n* https://www.kaggle.com/datasets/mahmoudreda55/satellite-image-classification\n* [satellite-image-classification-pytorch](https://github.com/dilaraozdemir/satellite-image-classification-pytorch)\n\n## Kaggle - EuroSAT - Sentinel-2 Dataset\n* https://www.kaggle.com/datasets/raoofnaushad/eurosat-sentinel2-dataset\n* RGB Land Cover and Land Use Classification using Sentinel-2 Satellite\n* Used in paper [Image Augmentation for Satellite Images](https://arxiv.org/abs/2207.14580)\n\n## Kaggle - Satellite Images of Water Bodies\n* https://www.kaggle.com/datasets/franciscoescobar/satellite-images-of-water-bodies\n* [pytorch-waterbody-segmentation](https://github.com/gauthamk02/pytorch-waterbody-segmentation) -\u003e UNET model trained on the Satellite Images of Water Bodies dataset from Kaggle. The model is deployed on Hugging Face Spaces\n\n## Kaggle - NOAA sea lion count\n* https://www.kaggle.com/c/noaa-fisheries-steller-sea-lion-population-count\n* [noaa](https://github.com/darraghdog/noaa) -\u003e UNET, object detection and image level regression approaches\n\n### Kaggle - miscellaneous\n* https://www.kaggle.com/datasets/reubencpereira/spatial-data-repo -\u003e Satellite + loan data\n* https://www.kaggle.com/datasets/towardsentropy/oil-storage-tanks -\u003e Image data of industrial oil tanks with bounding box annotations, estimate tank fill % from shadows\n* https://www.kaggle","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsatellite-image-deep-learning%2Fdatasets","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsatellite-image-deep-learning%2Fdatasets","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsatellite-image-deep-learning%2Fdatasets/lists"}