Datasets:
dataset_info:
features:
- name: rgb
dtype: image
- name: bands
dtype: binary
- name: bands_shape
list: int64
- name: bands_dtype
dtype: string
- name: band_metadata
dtype: string
- name: band_order
list: string
- 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
splits:
- name: train
num_bytes: 3001912034
num_examples: 27000
download_size: 2661440828
dataset_size: 3001912034
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- image-classification
size_categories:
- 10K<n<100K
Eurosat Lulc Classification
This dataset provides multispectral satellite imagery capturing diverse land use and land cover patterns across European landscapes. The real-world images, collected from satellite platforms, offer a comprehensive resource for training and evaluating computer vision models in environmental monitoring and land cover analysis. The dataset contains 27,000 images across 10 classes: AnnualCrop, Forest, HerbaceousVegetation, Highway, Industrial, Pasture, PermanentCrop, Residential, River, SeaLake.
Images per class:
- AnnualCrop: 3,000
- Forest: 3,000
- HerbaceousVegetation: 3,000
- Highway: 2,500
- Industrial: 2,500
- Pasture: 2,000
- PermanentCrop: 2,500
- Residential: 3,000
- River: 2,500
- SeaLake: 3,000
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
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},
publisher={IEEE}
}
Helber, P., Bischke, B., Dengel, A., & Borth, D. (2018). EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification [Dataset]. In EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification (Vol. 12, Issue 7, pp. 2217–2226). Zenodo. Introducing Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification, Valencia, Spain. https://doi.org/10.5281/zenodo.7711810
Sentinel data is open source. Please refer to https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice for terms of use.
This dataset was reformatted from its original format to match HuggingFace standards.