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

from datasets import load_dataset

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

Also, please see our 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}, 
}