---
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}
}
```