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

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