| --- |
| task_categories: |
| - image-segmentation |
| --- |
| # SegMunich |
| **SegMunich** is a segmentation task dataset that is Sentinel-2 satellite based. It contains spectral imagery of Munich's urban landscape over a span of three years. |
|
|
| Please refer to the original paper for more detailed information about the original SegMunich dataset: |
| - Paper: https://arxiv.org/abs/2311.07113 |
|
|
| ## How to Use This Dataset |
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset("GFM-Bench/SegMunich") |
| ``` |
|
|
| 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**: 10 |
| - **Sentinel-2 Bands**: B01 (**Coastal aerosol**), B02 (**Blue**), B03 (**Green**), B04 (**Red**), B05 (**Vegetation red edge**), B06 (**Vegetation red edge**), B07 (**Vegetation red edge**), B8A (**Narrow NIR**), B11 (**SWIR**), B12 (**SWIR**) |
| - **Image Resolution**: 128 x 128 pixels |
| - **Spatial Resolution**: 10 meters |
| - **Number of Classes**: 13 |
|
|
| ## Dataset Splits |
| The **SegMunich** dataset consists following splits: |
| - **train**: 3,000 samples |
| - **val**: 403 samples |
| - **test**: 403 samples |
|
|
| ## Dataset Features: |
| The **SegMunich** dataset consists of following features: |
| <!--- **radar**: the Sentinel-1 image.--> |
| - **optical**: the Sentinel-2 image. |
| - **label**: the segmentation labels. |
| <!--- **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 SegMunich dataset in your work, please cite the original paper: |
| ``` |
| @article{hong2024spectralgpt, |
| title={SpectralGPT: Spectral remote sensing foundation model}, |
| author={Hong, Danfeng and Zhang, Bing and Li, Xuyang and Li, Yuxuan and Li, Chenyu and Yao, Jing and Yokoya, Naoto and Li, Hao and Ghamisi, Pedram and Jia, Xiuping and others}, |
| journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, |
| year={2024}, |
| publisher={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}, |
| } |
| ``` |
