metadata

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': AnnualCrop
            '1': Forest
            '2': HerbaceousVegetation
            '3': Highway
            '4': Industrial
            '5': Pasture
            '6': PermanentCrop
            '7': Residential
            '8': River
            '9': SeaLake
    - name: image_id
      dtype: string
  splits:
    - name: train
      num_bytes: 55332279
      num_examples: 16200
    - name: validation
      num_bytes: 18472972.2
      num_examples: 5400
    - name: test
      num_bytes: 18625106.4
      num_examples: 5400
  download_size: 92078756
  dataset_size: 92430357.6
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
license: mit
size_categories:
  - 10K<n<100K
task_categories:
  - image-classification

EuroSat (RGB)

Description

A dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples. This is the RGB version of the dataset with visible bands encoded as JPEG images.

The dataset does not have any default splits. Train, validation, and test splits were based on these definitions here https://github.com/google-research/google-research/blob/master/remote_sensing_representations/README.md#dataset-splits

Website: https://github.com/phelber/eurosat
Paper: https://arxiv.org/abs/1709.00029

Citation

@article{helber2019eurosat,
  title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
  author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year={2019},
  publisher={IEEE}
}