/awesome-satellite-imagery-datasets

List of satellite imagery datasets with annotations for computer vision and deep learning

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, chip classification, other).

Instance Segmentation

  • CanadianBuildingFootprints (Microsoft, Mar 2019)
    12.6mil building footprints (all of Canada), GeoJSON format, delineation based on Bing imagery using ResNet34 architecture.

  • Spacenet Challenge Round 4 - Off-nadir (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.

  • 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 Challenge Round 2 - Buildings (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 Challenge Round 1 - Buildings (CosmiQ Works, Radiant Solutions, NVIDIA, Jan 2017)
    Building footprints (Rio de Janeiro), 3/8band Worldview-3 imagery (0.5m res.), SpaceNet Challenge Asset Library

Object Detection

Semantic Segmentation

Chip classification (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.

  • 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

  • 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

  • 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

  • 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

  • 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)

  • 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.)

Other Focus / Multiple Tasks