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