metadata

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

from datasets import load_dataset

dataset = load_dataset("GFM-Bench/SegMunich")

Also, please see our 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-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:

optical: the Sentinel-2 image.
label: the segmentation labels.
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}
}