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---
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
'5': pasture
'6': permanent crop
'7': residential
'8': river
'9': sea or lake
splits:
- name: train
num_bytes: 88391109
num_examples: 27000
download_size: 88591771
dataset_size: 88391109
license: mit
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "EuroSAT"
## Dataset Description
- **Paper** [Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification](https://ieeexplore.ieee.org/iel7/4609443/8789745/08736785.pdf)
- **Paper** [Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://ieeexplore.ieee.org/iel7/8496405/8517275/08519248.pdf)
- **GitHub** [EuroSAT](https://github.com/phelber/EuroSAT)
- **Data** [Zenodo](https://zenodo.org/record/7711810#.ZCcA9uzMLJx)
### Licensing Information
MIT.
## Citation Information
[Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification](https://ieeexplore.ieee.org/iel7/4609443/8789745/08736785.pdf)
[Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://ieeexplore.ieee.org/iel7/8496405/8517275/08519248.pdf)
```
@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},
year = 2019,
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
publisher = {IEEE}
}
@inproceedings{helber2018introducing,
title = {Introducing 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},
year = 2018,
booktitle = {IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
pages = {204--207},
organization = {IEEE}
}
``` |