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Awesome Open Source

Awesome Satellite Imagery Datasets Awesome

List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. Newest datasets at the top of each category (Instance segmentation, object detection, semantic segmentation, scene classification, other).

Recent additions and ongoing competitions

1. Instance Segmentation

  • SpaceNet 7: Multi-Temporal Urban Development Challenge (CosmiQ Works, Planet, Aug 2020)
    Monthly building footprints and Planet imagery (4m. res) timeseries for 2 years, 100 locations around the globe, for building footprint evolution & address propagation.

  • RarePlanes: Synthetic Data Takes Flight (CosmiQ Works, A.I.Reverie, June 2020)
    Synthetic (630k planes, 50k images) and real (14.7k planes, 253 Worldview-3 images (0.3m res.), 122 locations, 22 countries) plane annotations & properties and satellite images. Tools. Paper: Shermeyer et al. 2020

  • SpaceNet: Multi-Sensor All-Weather Mapping (CosmiQ Works, Capella Space, Maxar, AWS, Intel, Feb 2020)
    48k building footprints (enhanced 3DBAG dataset, building height attributes), Capella Space SAR data (0.5m res., four polarizations) & Worldview-3 imagery (0.3m res.), Rotterdam, Netherlands.

  • Agriculture-Vision Database & CVPR 2020 challenge (UIUC, Intelinair, CVPR, Jan 2020)
    Agricultural Pattern Analysis, 21k aerial farmland images (RGB-NIR, USA, 2019 season, 512x512px chips), label masks for 6 field anomaly patterns (Cloud shadow, Double plant, Planter skip, Standing Water, Waterway and Weed cluster). Paper: Chiu et al. 2020

  • iSAID: Large-scale Dataset for Object Detection in Aerial Images (IIAI & Wuhan University, Dec 2019)
    15 categories from plane to bridge, 188k instances, object instances and segmentation masks (MS COCO format), Google Earth & JL-1 image chips, Faster-RCNN baseline model (MXNet), devkit, Academic use only, replaces DOTA dataset, Paper: Zamir et al. 2019

  • xView 2 Building Damage Asessment Challenge (DIUx, Nov 2019) .
    550k building footprints & 4 damage scale categories, 20 global locations and 7 disaster types (wildfire, landslides, dam collapses, volcanic eruptions, earthquakes/tsunamis, wind, flooding), Worldview-3 imagery (0.3m res.), pre-trained baseline model. Paper: Gupta et al. 2019

  • Microsoft BuildingFootprints Canada & USA & Uganda/Tanzania & Australia (Microsoft, Mar 2019)
    12.6mil (Canada) & 125.2mil (USA) & 17.9mil (Uganda/Tanzania) & 11.3mil (Australia) building footprints, GeoJSON format, delineation based on Bing imagery using ResNet34 architecture.

  • SpaceNet 4: Off-Nadir Buildings (CosmiQ Works, DigitalGlobe, Radiant Solutions, AWS, Dec 2018)
    126k building footprints (Atlanta), 27 WorldView 2 images (0.3m res.) from 7-54 degrees off-nadir angle. Bi-cubicly resampled to same number of pixels in each image to counter courser native resolution with higher off-nadir angles, Paper: Weir et al. 2019

  • Airbus Ship Detection Challenge (Airbus, Nov 2018)
    131k ships, 104k train / 88k test image chips, satellite imagery (1.5m res.), raster mask labels in in run-length encoding format, Kaggle kernels.

  • Open AI Challenge: Tanzania (WeRobotics & Wordlbank, Nov 2018)
    Building footprints & 3 building conditions, RGB UAV imagery - Link to data

  • Netherlands LPIS agricultural field boundaries (Netherlands Department for Economic Affairs)
    294 crop/vegetation catgeories, 780k parcels, yearly dataset for 2009-2018. Open the atom feed downloadlinks with Firefox etc., not Chrome.

  • Denmark LPIS agricultural field boundaries (Denmark Department for Agriculture)
    293 crop/vegetation catgeories, 600k parcels, yearly dataset for 2008-2018

  • CrowdAI Mapping Challenge (Humanity & Inclusion NGO, May 2018)
    Buildings footprints, RGB satellite imagery, COCO data format

  • SpaceNet 2: Building Detection v2 (CosmiQ Works, Radiant Solutions, NVIDIA, May 2017)
    685k building footprints, 3/8band Worldview-3 imagery (0.3m res.), 5 cities, SpaceNet Challenge Asset Library

  • SpaceNet 1: Building Detection v1 (CosmiQ Works, Radiant Solutions, NVIDIA, Jan 2017)
    Building footprints (Rio de Janeiro), 3/8band Worldview-3 imagery (0.5m res.), SpaceNet Challenge Asset Library

2. Object Detection

3. Semantic Segmentation

4. Scene classification (chip/Image recognition)

  • BigEarthNet: Large-Scale Sentinel-2 Benchmark (TU Berlin, Jan 2019)
    Multiple landcover labels per chip based on CORINE Land Cover (CLC) 2018, 590,326 chips from Sentinel-2 L2A scenes (125 Sentinel-2 tiles from 10 European countries, 2017/2018), 66 GB archive, Paper: Sumbul et al. 2019

  • WiDS Datathon 2019 : Detection of Oil Palm Plantations (Global WiDS Team & West Big Data Innovation Hub, Jan 2019) Prediction of presence of oil palm plantations, Planet satellite imagery (3m res.)., ca. 20k 256 x 256 pixel chips, 2 categories oil-palm and other, annotator confidence score.

  • So2Sat LCZ42 (TUM Munich & DLR, Aug 2018)
    Local climate zone classification, 17 categories (10 urban e.g. compact high-rise, 7 rural e.g. scattered trees), 400k 32x32 pixel chips covering 42 cities (LCZ42 dataset), Sentinel 1 & Sentinel 2 (both 10m res.), 51 GB

  • Cactus Aerial Photos (CONACYT Mexico, Jun 2018)
    17k aerial photos, 13k cactus, 4k non-actus, Kaggle kernels, Paper: López-Jiménez et al. 2019

  • Statoil/C-CORE Iceberg Classifier Challenge (Statoil/C-CORE, Jan 2018)
    2 categories ship and iceberg, 2-band HH/HV polarization SAR imagery, Kaggle kernels

  • Functional Map of the World Challenge (IARPA, Dec 2017)
    63 categories from solar farms to shopping malls, 1 million chips, 4/8 band satellite imagery (0.3m res.), COCO data format, baseline models, Paper: Christie et al. 2017

  • EuroSAT (DFK, Aug 2017)
    10 land cover categories from industrial to permanent crop, 27k 64x64 pixel chips, 3/16 band Sentinel-2 satellite imagery (10m res.), covering cities in 30 countries, Paper: Helber et al. 2017

  • Planet: Understanding the Amazon from Space (Planet, Jul 2017)
    13 land cover categories + 4 cloud condition categories, 4-band (RGB-NIR) satelitte imagery (5m res.), Amazonian rainforest, Kaggle kernels

  • RESISC45 (Northwestern Polytechnical University NWPU, Mar 2017)
    45 scene categories from airplane to wetland, 31,500 images (700 per category, 256x256 px), image chips taken from Google Earth (rich image variations in resolution, angle, geography all over the world), Download Link, Paper: Cheng et al. 2017

  • Deepsat: SAT-4/SAT-6 airborne datasets (Louisiana State University, 2015)
    6 land cover categories, 400k 28x28 pixel chips, 4-band RGBNIR aerial imagery (1m res.) extracted from the 2009 National Agriculture Imagery Program (NAIP), Paper: Basu et al. 2015

  • UC Merced Land Use Dataset (UC Merced, Oct 2010)
    21 land cover categories from agricultural to parkinglot, 100 chips per class, aerial imagery (0.30m res.), Paper: Yang & Newsam 2010

5. Other Focus / Multiple Tasks

  • IEEE Data Fusion Contest 2020 (IEEE & TUM, Mar 2020)
    Land cover classification based on SEN12MS dataset (see category Semantic Segmentation on this list), low- and high-resolution tracks.

  • IEEE Data Fusion Contest 2019 (IEEE, Mar 2019)
    Multiple tracks: Semantic 3D reconstruction, Semantic Stereo, 3D-Point Cloud Classification. Worldview-3 (8-band, 0.35cm res.) satellite imagery, LiDAR (0.80m pulse spacing, ASCII format), semantic labels, urban setting USA, baseline methods provided, Paper: Le Saux et al. 2019

  • IEEE Data Fusion Contest 2018 (IEEE, Mar 2018)
    20 land cover categories by fusing three data sources: Multispectral LiDAR, Hyperspectral (1m), RGB imagery (0.05m res.)

  • DEEPGLOBE - 2018 Satellite Challange (CVPR, Apr 2018)
    Three challenge tracks: Road Extraction, Building Detection, Land cover classification, Paper: Demir et al. 2018

  • TiSeLaC : Time Series Land Cover Classification Challenge (UMR TETIS, Jul 2017)
    Land cover time series classification (9 categories), Landsat-8 (23 images time series, 10 band features, 30m res.), Reunion island

  • Multi-View Stereo 3D Mapping Challenge (IARPA, Nov 2016)
    Develop a Multi-View Stereo (MVS) 3D mapping algorithm that can convert high-resolution Worldview-3 satellite images to 3D point clouds, 0.2m lidar ground truth data.

  • Draper Satellite Image Chronology (Draper, Jun 2016)
    Predict the chronological order of images taken at the same locations over 5 days, Kaggle kernels

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