|
--- |
|
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} |
|
} |
|
``` |
