metadata

language: en
license: unknown
size_categories:
  - 10K<n<100K
task_categories:
  - image-classification
paperswithcode_id: eurosat
pretty_name: EuroSAT RGB
tags:
  - remote-sensing
  - earth-observation
  - geospatial
  - satellite-imagery
  - land-cover-classification
  - sentinel-2
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Annual Crop
            '1': Forest
            '2': Herbaceous Vegetation
            '3': Highway
            '4': Industrial Buildings
            '5': Pasture
            '6': Permanent Crop
            '7': Residential Buildings
            '8': River
            '9': SeaLake
    - name: filename
      dtype: string
  splits:
    - name: train
      num_bytes: 104485303
      num_examples: 16200
    - name: test
      num_bytes: 34726245
      num_examples: 5400
    - name: validation
      num_bytes: 34781690
      num_examples: 5400
  download_size: 174279561
  dataset_size: 173993238
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: validation
        path: data/validation-*

EuroSAT RGB

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

Paper: https://arxiv.org/abs/1709.00029
Homepage: https://github.com/phelber/EuroSAT

Description

The EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the ESA Sentinel-2 satellite. It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.

The dataset is available in two versions: RGB only (this repo) and all 13 Multispectral (MS) Sentinel-2 bands. EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.

Total Number of Images: 27000
Bands: 3 (RGB)
Image Resolution: 64x64m
Land Cover Classes: 10
Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake

Usage

To use this dataset, simply use datasets.load_dataset("blanchon/EuroSAT_RGB").

from datasets import load_dataset
EuroSAT_RGB = load_dataset("blanchon/EuroSAT_RGB")

Citation

If you use the EuroSAT dataset in your research, please consider citing the following publication:

@article{helber2017eurosat,
   title={EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},
   author={Helber, et al.},
   journal={ArXiv preprint arXiv:1709.00029},
   year={2017}
}