---
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}, 
}
```