language:en license:mit region:us
**Homepage:** https://github.com/phelber/EuroSAT

**IMPORTANT NOTES**
- This HF dataset downloads the RGB images of the EuroSAT dataset: https://zenodo.org/record/7711810#.ZAm3k-zMKEA; i.e., the EuroSAT_RGB.zip
@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},
  year={2019},
  publisher={IEEE}
}
---
pretty_name: 'EuroSAT (RGB)'
language:
- en
license: mit
---
# Dataset Card for EuroSAT Dataset (RGB) 

## Dataset Description

**Homepage:** https://github.com/phelber/EuroSAT

**IMPORTANT NOTES**
- This HF dataset downloads the RGB images of the EuroSAT dataset: https://zenodo.org/record/7711810#.ZAm3k-zMKEA; i.e., the EuroSAT_RGB.zip

**Paper Citation:** 
```
@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},
  year={2019},
  publisher={IEEE}
}
```
```
@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}
}
```

## Dataset Summary
In this study, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible provided in the Earth observation program Copernicus. We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images. We provide benchmarks for this novel dataset with its spectral bands using state-of-the-art deep Convolutional Neural Network (CNNs). With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The resulting classification system opens a gate towards a number of Earth observation applications. We demonstrate how this classification system can be used for detecting land use and land cover changes and how it can assist in improving geographical maps. The geo-referenced dataset EuroSAT is made publicly available [here](https://github.com/phelber/EuroSAT).