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---
task_categories:
- image-classification
---
# BigEarthNet
**BigEarthNet** is a large-scale benchmark dataset for multi-label classification, derived from Sentinel-1 (radar) and Sentinel-2 (optical) satellite imagery.
We have pre-processed the dataset by upsampling all sentinel-2 channels to 120x120 pixels and concatenated them together. Please see [Torchgeo/bigearthnet](https://github.com/microsoft/torchgeo/blob/main/torchgeo/datasets/bigearthnet.py#L385) for more information about pre-processing. In addition, we map the original 43 land cover classes to 19 broader categories using a predefined conversion scheme.
## How to Use This Dataset
```python
from datasets import load_dataset
dataset = load_dataset("GFM-Bench/BigEarthNet")
```
Also, please see our [GFM-Bench](https://github.com/uiuctml/GFM-Bench) repository for more information about how to use the dataset! 🤗
## Dataset Metadata
The following metadata provides details about the Sentinel-2 imagery used in the dataset:
- **Number of Sentinel-1 Bands**: 2
- **Sentinel-1 Bands**: VV, VH
- **Number of Sentinel-2 Bands**: 12
- **Sentinel-2 Bands**: B01 (**Coastal aerosol**), B02 (**Blue**), B03 (**Green**), B04 (**Red**), B05 (**Vegetation red edge**), B06 (**Vegetation red edge**), B07 (**Vegetation red edge**), B08 (**NIR**), B8A (**Narrow NIR**), B09 (**Water vapour**), B11 (**SWIR**), B12 (**SWIR**)
- **Image Resolution**: 120 x 120 pixels
- **Spatial Resolution**: 10 meters
- **Number of Classes**: 19
- **Class Labels**:
- Urban fabric
- Industrial or commercial units
- Arable land
- Permanent crops
- Pastures
- Complex cultivation patterns
- Land principally occupied by agriculture, with significant areas of natural vegetation
- Agro-forestry areas
- Broad-leaved forest
- Coniferous forest
- Mixed forest
- Natural grassland and sparsely vegetated areas
- Moors, heathland and sclerophyllous vegetation
- Transitional woodland, shrub
- Beaches, dunes, sands
- Inland wetlands
- Coastal wetlands
- Inland waters
- Marine waters
## Dataset Splits
The **BigEarthNet** dataset consists following splits:
- **train**: 269,695 samples
- **val**: 123,723 samples
- **test**: 125,866 samples
## Dataset Features:
The **BigEarthNet** dataset consists of following features:
- **radar**: the Sentinel-1 image.
- **optical**: the Sentinel-2 image.
- **label**: the classification label.
- **radar_channel_wv**: the central wavelength of each Sentinel-1 bands.
- **optical_channel_wv**: the central wavelength of each Sentinel-2 bands.
- **spatial_resolution**: the spatial resolution of images.
## Citation
If you use the BigEarthNet dataset in your work, please cite original papers:
```
@inproceedings{sumbul2019bigearthnet,
title={Bigearthnet: A large-scale benchmark archive for remote sensing image understanding},
author={Sumbul, Gencer and Charfuelan, Marcela and Demir, Beg{\"u}m and Markl, Volker},
booktitle={IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium},
pages={5901--5904},
year={2019},
organization={IEEE}
}
```
and if you also find our benchmark useful, please consider citing our paper:
```
@misc{si2025scalablefoundationmodelmultimodal,
title={Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data},
author={Haozhe Si and Yuxuan Wan and Minh Do and Deepak Vasisht and Han Zhao and Hendrik F. Hamann},
year={2025},
eprint={2503.12843},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2503.12843},
}
``` |