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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](https://github.com/uiuctml/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:
```python
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:
```python
@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}
}
``` |