Datasets:
File size: 1,594 Bytes
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---
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}
}
``` |