BigEarthNet Dataset

The BigEarthNet dataset is a large-scale benchmark Archive for remoting sensing. The dataset contains both Sentinel-2 and Sentinel-1 imagery.

We have pre-processed the dataset by upsampling all sentinel-2 channels to 120x120 pixels and concatenated them together. Please see Torchgeo/bigearthnet for more information about pre-processing.

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

Metadata

The following metadata provides details about the Sentinel-2 and Sentinel-1 imagery used in the dataset:

S2_MEAN = [752.40087073, 884.29673756, 1144.16202635, 1297.47289228, 1624.90992062, 2194.6423161, 2422.21248945, 2517.76053101, 2581.64687018, 2645.51888987, 2368.51236873, 1805.06846033]
S2_STD = [1108.02887453, 1155.15170768, 1183.6292542, 1368.11351514, 1370.265037, 1355.55390699, 1416.51487101, 1474.78900051, 1439.3086061, 1582.28010962, 1455.52084939, 1343.48379601]
S1_MEAN = [-12.54847273, -20.19237134]
S1_STD = [5.25697717, 5.91150917]

metadata = {
    "s2c": {
        "bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B11", "B12"],
        "channel_wv": [442.7, 492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 945.1, 1613.7, 2202.4],
        "mean": S2_MEAN,
        "std": S2_STD
    },
    "s1": {
        "bands": ["VV", "VH"],
        "channel_wv": [5500, 5700],
        "mean": S1_MEAN,
        "std": S1_STD
    }
}

SIZE = HEIGHT = WIDTH = 120

NUM_CLASSES = 19

spatial_resolution = 10

Split

The BigEarthNet dataset consists splits of:

• train: 269,695 samples.
• val: 123,723 samples.
• test: 125,866 samples.

Features:

The BigEarthNet dataset consists of following features:

• optical: the Sentinel-2 image.
• radar: the Sentinel-1 image.
• label: the classification label.
• optical_channel_wv: the wavelength of each optical channel.
• radar_channel_wv: the wavelength of each radar channel.
• spatial_resolution: the spatial resolution of images.

Citation

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

@inproceedings{sumbul2019bigearthnet,
  title={Bigearthnet: A large-scale benchmark archive for remote sensing image understanding},
  author={Sumbul, Gencer and Charfuelan, Marcela and Demir, Beg{\"u}m and Markl, Volker},
  booktitle={IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium},
  pages={5901--5904},
  year={2019},
  organization={IEEE}
}