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

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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

Object Detection

Semantic Segmentation

  • Land Cover Classification (Sinergise, Feb 2019)
    10 land cover classes, temporal stack of hyperspectral Sentinel-2 imagery (R,G,B,NIR,SWIR1,SWIR2; 10 m res.) for year 2017 with cloud masks, Official Slovenian land use land cover layer as ground truth.

  • Agricultural Crop Cover Classification Challenge (CrowdANALYTIX, Jul 2018)
    2 main categories corn and soybeans, Landsat 8 imagery (30m res.), USDA Cropland Data Layer as ground truth.

  • Spacenet Challenge Round 3 - Roads (CosmiQ Works, Radiant Solutions, Feb 2018)
    8000 km of roads in 5 city aois, 3/8band Worldview-3 imagery (0.3m res.), SpaceNet Challenge Asset Library

  • Urban 3D Challenge (USSOCOM, Dec 2017)
    157k building footprint masks, RGB orthophotos (0.5m res.), DSM/DTM, 3 cities, SpaceNet Challenge Asset Library

  • DSTL Satellite Imagery Feature Detection Challenge (Dstl, Feb 2017)
    10 land cover categories from crops to vehicle small, 57 1x1km images, 3/16-band Worldview 3 imagery (0.3m-7.5m res.), Kaggle kernels

  • Inria Aerial Image Labeling (inria)
    Building footprint masks, RGB aerial imagery (0.3m res.), 5 cities

  • ISPRS Potsdam 2D Semantic Labeling Contest (ISPRS)
    6 urban land cover classes, raster mask labels, 4-band RGB-IR aerial imagery (0.05m res.) & DSM, 38 image patches

Chip classification (Image Recognition)

  • Alibaba Cloud German AI Challenge 2019 (StepStone, DLR, Alibaba Cloud, Tianchi, Jan 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 2 & Sentinel 1 (both 10m res.)

  • 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

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

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