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--- |
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language: |
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- en |
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tags: |
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- clouds |
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- sentinel-2 |
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- image-segmentation |
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- deep-learning |
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- remote-sensing |
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pretty_name: cloudsen12 |
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--- |
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# cloudsen12 |
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***``A dataset about clouds from Sentinel-2``*** |
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CloudSEN12 is a LARGE dataset (~1 TB) for cloud semantic understanding that consists of 49,400 image patches (IP) that are evenly spread throughout all continents except Antarctica. Each IP covers 5090 x 5090 meters and contains data from Sentinel-2 levels 1C and 2A, hand-crafted annotations of thick and thin clouds and cloud shadows, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from six cutting-edge cloud detection algorithms. |
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CloudSEN12 is designed to support both weakly and self-/semi-supervised learning strategies by including three distinct forms of hand-crafted labeling data: high-quality, scribble and no-annotation. For more details on how we created the dataset see our paper: CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2. |
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**ML-STAC Snippet** |
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```python |
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import mlstac |
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secret = 'https://huggingface.co/datasets/jfloresf/mlstac-demo/resolve/main/main.json' |
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train_db = mlstac.load(secret, framework='torch', stream=True, device='cpu') |
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``` |
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<p align="center"> |
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<img src="header.png" /> |
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</p> |
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**Sensor: Sentinel2 - MSI** |
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**ML-STAC Task: TensorToTensor, TensorSegmentation** |
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**Data raw repository: [https://cloudsen12.github.io/](https://cloudsen12.github.io/)** |
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**Dataset discussion: [https://github.com/IPL-UV/ML-STAC/discussions/2](https://github.com/IPL-UV/ML-STAC/discussions/2)** |
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**Review mean score: 5.0** |
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**Split_strategy: random** |
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**Paper: [https://www.nature.com/articles/s41597-022-01878-2](https://www.nature.com/articles/s41597-022-01878-2)** |
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## Data Providers |
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|Name|Role|URL| |
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| :---: | :---: | :---: | |
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|Image & Signal Processing|['host']|https://isp.uv.es/| |
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|ESA|['producer']|https://www.esa.int/| |
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## Curators |
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|Name|Organization|URL| |
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| :---: | :---: | :---: | |
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|Jair Flores|OEFA|http://jflores.github.io/| |
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## Reviewers |
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|Name|Organization|URL|Score| |
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| :---: | :---: | :---: | :---: | |
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|Cesar Aybar|Image & Signal Processing|http://csaybar.github.io/|5| |
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## Labels |
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|Name|Value| |
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| :---: | :---: | |
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|clear|0| |
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|thick-cloud|1| |
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|thin-cloud|2| |
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|cloud-shadow|3| |
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## Dimensions |
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### input |
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|Axis|Name|Description| |
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| :---: | :---: | :---: | |
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|0|C|Spectral bands| |
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|1|H|Height| |
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|2|W|Width| |
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### target |
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|Axis|Name|Description| |
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| :---: | :---: | :---: | |
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|0|C|Hand-crafted labels| |
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|1|H|Height| |
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|2|W|Width| |
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## Spectral Bands |
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|Name|Common Name|Description|Center Wavelength|Full Width Half Max|Index| |
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| :---: | :---: | :---: | :---: | :---: | :---: | |
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|B01|coastal aerosol|Band 1 - Coastal aerosol - 60m|443.5|17.0|0| |
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|B02|blue|Band 2 - Blue - 10m|496.5|53.0|1| |
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|B03|green|Band 3 - Green - 10m|560.0|34.0|2| |
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|B04|red|Band 4 - Red - 10m|664.5|29.0|3| |
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|B05|red edge 1|Band 5 - Vegetation red edge 1 - 20m|704.5|13.0|4| |
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|B06|red edge 2|Band 6 - Vegetation red edge 2 - 20m|740.5|13.0|5| |
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|B07|red edge 3|Band 7 - Vegetation red edge 3 - 20m|783.0|18.0|6| |
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|B08|NIR|Band 8 - Near infrared - 10m|840.0|114.0|7| |
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|B8A|red edge 4|Band 8A - Vegetation red edge 4 - 20m|864.5|19.0|8| |
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|B09|water vapor|Band 9 - Water vapor - 60m|945.0|18.0|9| |
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|B10|cirrus|Band 10 - Cirrus - 60m|1375.5|31.0|10| |
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|B11|SWIR 1|Band 11 - Shortwave infrared 1 - 20m|1613.5|89.0|11| |
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|B12|SWIR 2|Band 12 - Shortwave infrared 2 - 20m|2199.5|173.0|12| |
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