---
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
    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-*
license: mit
size_categories:
- 10K<n<100K
task_categories:
- image-classification
---

# 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.

The dataset does not have any default splits. Train, validation, and test splits were based on these definitions here https://github.com/google-research/google-research/blob/master/remote_sensing_representations/README.md#dataset-splits

* 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}
}
```