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README.md
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---
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task_categories:
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- image-classification
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---
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# So2Sat
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**So2Sat** is a local climate zone (LCZ) classification task consisting of images from Sentinel-1 and Sentinel-2. We only keep Sentinel-2 images which have 10 MSI bands in this dataset. In addition, we reserve 10% of the training set as the validation set, resulting in 31,713 training samples, 3,523 validation samples and 48,307 test samples.
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## How to Use This Dataset
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```python
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from datasets import load_dataset
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dataset = load_dataset("GFM-Bench/So2Sat")
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```
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Also, please see our [GFM-Bench](https://github.com/uiuctml/GFM-Bench) repository for more information about how to use the dataset! 🤗
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## Dataset Metadata
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The following metadata provides details about the Sentinel-2 imagery used in the dataset:
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<!--- **Number of Sentinel-1 Bands**: 2-->
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<!--- **Sentinel-1 Bands**: VV, VH-->
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- **Number of Sentinel-2 Bands**: 10
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- **Sentinel-2 Bands**: B02 (**Blue**), B03 (**Green**), B04 (**Red**), B05 (**Vegetation red edge**), B06 (**Vegetation red edge**), B07 (**Vegetation red edge**), B08 (**NIR**), B8A (**Narrow NIR**), B11 (**SWIR**), B12 (**SWIR**)
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- **Image Resolution**: 32 x 32 pixels
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- **Spatial Resolution**: 10 meters
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- **Number of Classes**: 17
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## Dataset Splits
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The **So2Sat** dataset consists following splits:
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- **train**: 31,713 samples
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- **val**: 3,523 samples
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- **test**: 48,307 samples
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## Dataset Features:
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The **So2Sat** dataset consists of following features:
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<!--- **radar**: the Sentinel-1 image.-->
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- **optical**: the Sentinel-2 image.
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- **label**: the classification label.
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<!--- **radar_channel_wv**: the central wavelength of each Sentinel-1 bands.-->
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- **optical_channel_wv**: the central wavelength of each Sentinel-2 bands.
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- **spatial_resolution**: the spatial resolution of images.
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## Citation
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If you use the So2Sat dataset in your work, please cite the original paper:
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```
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@article{zhu2019so2sat,
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title={So2Sat LCZ42: A benchmark dataset for global local climate zones classification},
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author={Zhu, Xiao Xiang and Hu, Jingliang and Qiu, Chunping and Shi, Yilei and Kang, Jian and Mou, Lichao and Bagheri, Hossein and H{\"a}berle, Matthias and Hua, Yuansheng and Huang, Rong and others},
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journal={arXiv preprint arXiv:1912.12171},
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year={2019}
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}
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```
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