README.md

---
task_categories:
- image-classification
---
# EuroSAT 
**EuroSAT** is a benchmark dataset for land use and land cover classification based on Sentinel-2 satellite imagery. It contains 27,000 labeled images covering 10 classes (e.g., agricultural, residential, industrial, and forest areas). The dataset features multi-spectral bands with a spatial resolution of 10 meters per pixel and an image resolution of 64 × 64 pixels. 

## How to Use This Dataset
```python
from datasets import load_dataset

dataset = load_dataset("GFM-Bench/EuroSAT")
```

Also, please see our [GFM-Bench](https://github.com/uiuctml/GFM-Bench) repository for more information about how to use the dataset! 🤗

## Dataset Metadata

The following metadata provides details about the Sentinel-2 imagery used in the dataset:
- **Number of Sentinel-2 Bands**: 13
- **Sentinel-2 Bands**: B01 (**Coastal aerosol**), B02 (**Blue**), B03 (**Green**), B04 (**Red**), B05 (**Vegetation red edge**), B06 (**Vegetation red edge**), B07 (**Vegetation red edge**), B08 (**NIR**), B8A (**Narrow NIR**), B09 (**Water vapour**), B10 (**SWIR – Cirrus**), B11 (**SWIR**), B12 (**SWIR**)
- **Image Resolution**: 64 x 64 pixels
- **Spatial Resolution**: 10 meters 
- **Number of Classes**: 10 
- **Class Labels**: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake

## Dataset Splits
The **EuroSAT** dataset consists following splits:
- **train**: 16200 samples
- **val**: 5400 samples
- **test**: 5400 samples

## Dataset Features:
The **EuroSAT** dataset consists of following features:
- **optical**: the Sentinel-2 image.
- **label**: the classification label.
- **optical_channel_wv**: the central wavelength of each optical channel.
- **spatial_resolution**: the spatial resolution of images.
## Citation
If you use the EuroSAT dataset in your work, please cite original papers:
```
@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}
}
```
and
```
@inproceedings{helber2018introducing,
  title={Introducing 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},
  booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
  pages={204--207},
  year={2018},
  organization={IEEE}
}
```
and if you also find our benchmark useful, please consider citing our paper:
```
@misc{si2025scalablefoundationmodelmultimodal,
      title={Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data}, 
      author={Haozhe Si and Yuxuan Wan and Minh Do and Deepak Vasisht and Han Zhao and Hendrik F. Hamann},
      year={2025},
      eprint={2503.12843},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.12843}, 
}
```