EuroSAT / README.md
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task_categories:
  - image-classification

EuroSAT Dataset

The EuroSAT dataset consists of satellite imagery for land use and land cover classification. It contains labeled images of 10 different land cover classes.

Please see our GFM-Bench for more information about how to use the dataset! 🙂

Metadata

The following metadata provides details about the Sentinel-2 imagery used in the dataset:

S2_MEAN = [1354.40546513, 1118.24399958, 1042.92983953, 947.62620298, 1199.47283961, 1999.79090914, 2369.22292565, 2296.82608323, 732.08340178, 12.11327804, 1819.01027855, 1118.92391149, 2594.14080798]
S2_STD = [245.71762908, 333.00778264, 395.09249139, 593.75055589, 566.4170017, 861.18399006, 1086.63139075, 1117.98170791, 404.91978886, 4.77584468, 1002.58768311, 761.30323499, 1231.58581042]

metadata = {
    "s2c": {
        "bands": ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B10", "B11", "B12"],
        "channel_wv": [442.7, 492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 945.1, 1373.5, 1613.7, 2202.4],
        "mean": S2_MEAN,
        "std": S2_STD,
    },
    "s1": {
        "bands": None,
        "channel_wv": None,
        "mean": None,
        "std": None   
    }
}

SIZE = HEIGHT = WIDTH = 64

NUM_CLASSES = 10

spatial_resolution = 10

Split

The EuroSAT dataset consists splits of:

  • train: 16200 samples
  • val: 5400 samples
  • test: 5400 samples

Features:

The EuroSAT dataset consists of following features:

  • optical: the Sentinel-2 image.
  • label: the classification label.
  • optical_channel_wv: the wavelength of each optical channel.
  • spatial_resolution: the spatial resolution of images.

Citation

If you use the EuroSAT dataset in your work, please cite the original paper:

@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},
  volume={12},
  number={7},
  pages={2217--2226},
  year={2019},
  publisher={IEEE}
}