README.md

metadata

license: mit
task_categories:
  - image-classification
task_ids:
  - multi-class-image-classification
language:
  - en
tags:
  - remote-sensing
  - satellite-imagery
  - land-use
  - land-cover
  - sentinel-2
  - earth-observation
  - eurosat
pretty_name: EuroSAT RGB
size_categories:
  - 10K<n<100K
source_datasets:
  - original

EuroSAT RGB

Dataset Description

EuroSAT is a dataset for land use and land cover (LULC) classification using Sentinel-2 satellite imagery. This version contains the RGB (visible spectrum) bands encoded as JPEG images at 64x64 pixel resolution.

The dataset covers 10 land use/land cover classes across 27,000 geo-referenced images from 34 European countries.

• Source: https://zenodo.org/records/7711810
• DOI: 10.5281/zenodo.7711810
• License: MIT
• Paper: EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

Authors

Patrick Helber, Benjamin Bischke, Andreas Dengel, Damian Borth

Dataset Structure

Splits

Split	Examples
train	18,900
validation	5,400
test	2,700

Classes

Label	Class Name
0	AnnualCrop
1	Forest
2	HerbaceousVegetation
3	Highway
4	Industrial
5	Pasture
6	PermanentCrop
7	Residential
8	River
9	SeaLake

Features

• image: 64x64 RGB JPEG satellite image
• label: Integer class label (0–9)
• filename: Original filename with class directory prefix

Usage

from datasets import load_dataset

dataset = load_dataset("giswqs/EuroSAT_RGB")

# Access training split
train = dataset["train"]
print(train[0])

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},
  volume={12},
  number={7},
  pages={2217--2226},
  year={2019},
  doi={10.1109/JSTARS.2019.2918242},
  publisher={IEEE}
}