---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': AnnualCrop
          '1': Forest
          '2': HerbaceousVegetation
          '3': Highway
          '4': Industrial
          '5': Pasture
          '6': PermanentCrop
          '7': Residential
          '8': River
          '9': SeaLake
  - name: image_id
    dtype: string
  splits:
  - name: train
    num_bytes: 55332279.0
    num_examples: 16200
  - name: validation
    num_bytes: 18472972.2
    num_examples: 5400
  - name: test
    num_bytes: 18625106.4
    num_examples: 5400
  download_size: 92078756
  dataset_size: 92430357.6
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

# EuroSat (RGB)

## Description

A dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples. This is the RGB version of the dataset with visible bands encoded as JPEG images.

* Website: https://github.com/phelber/eurosat
* Paper: https://arxiv.org/abs/1709.00029


## Citation
```bibtext
@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}
}
```