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--- |
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task_categories: |
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- image-classification |
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--- |
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# EuroSAT Dataset |
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The **EuroSAT** dataset consists of satellite imagery for land use and land cover classification. It contains labeled images of 10 different land cover classes. |
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Please see our [GFM-Bench](https://github.com/uiuctml/GFM-Bench) for more information about how to use the dataset! 🙂 |
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## Metadata |
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The following metadata provides details about the Sentinel-2 imagery used in the dataset: |
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```python |
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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] |
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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] |
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metadata = { |
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"s2c": { |
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"bands": ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B10", "B11", "B12"], |
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"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], |
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"mean": S2_MEAN, |
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"std": S2_STD, |
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}, |
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"s1": { |
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"bands": None, |
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"channel_wv": None, |
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"mean": None, |
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"std": None |
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} |
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} |
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SIZE = HEIGHT = WIDTH = 64 |
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NUM_CLASSES = 10 |
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spatial_resolution = 10 |
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``` |
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## Split |
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The **EuroSAT** dataset consists splits of: |
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- **train**: 16200 samples |
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- **val**: 5400 samples |
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- **test**: 5400 samples |
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## Features: |
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The **EuroSAT** dataset consists of following features: |
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- **optical**: the Sentinel-2 image. |
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- **label**: the classification label. |
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- **optical_channel_wv**: the wavelength of each optical channel. |
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- **spatial_resolution**: the spatial resolution of images. |
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## Citation |
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If you use the EuroSAT dataset in your work, please cite the original paper: |
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```python |
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@article{helber2019eurosat, |
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title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification}, |
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author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian}, |
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journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, |
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volume={12}, |
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number={7}, |
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pages={2217--2226}, |
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year={2019}, |
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publisher={IEEE} |
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} |
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``` |
