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license: cc-by-nc-4.0 |
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# **CloudSEN12** |
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## **A Benchmark Dataset for Cloud Semantic Understanding** |
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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 |
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evenly spread throughout all continents except Antarctica. Each IP covers 5090 x 5090 meters and contains data from Sentinel-2 |
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levels 1C and 2A, hand-crafted annotations of thick and thin clouds and cloud shadows, Sentinel-1 Synthetic Aperture Radar (SAR), |
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digital elevation model, surface water occurrence, land cover classes, and cloud mask results from six cutting-edge |
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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 |
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hand-crafted labeling data: high-quality, scribble and no-annotation. For more details on how we created the dataset see our |
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paper. |
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Ready to start using **[CloudSEN12](https://cloudsen12.github.io/)**? |
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**[Download Dataset](https://cloudsen12.github.io/download.html)** |
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**[Paper - Scientific Data](https://www.nature.com/articles/s41597-022-01878-2)** |
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**[Inference on a new S2 image](https://colab.research.google.com/github/cloudsen12/examples/blob/master/example02.ipynb)** |
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**[Enter to cloudApp](https://github.com/cloudsen12/CloudApp)** |
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**[CloudSEN12 in Google Earth Engine](https://gee-community-catalog.org/projects/cloudsen12/)** |
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### **Example** |
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```py |
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import numpy as np |
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# Read high-quality train |
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train_shape = (8490, 512, 512) |
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B4X = np.memmap('train_B04.dat', dtype='int16', mode='r', shape=train_shape) |
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y = np.memmap('train_target.dat', dtype='int8', mode='r', shape=train_shape) |
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# Read high-quality val |
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val_shape = (535, 512, 512) |
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B4X = np.memmap('val_B04.dat', dtype='int16', mode='r', shape=val_shape) |
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y = np.memmap('val_target.dat', dtype='int8', mode='r', shape=val_shape) |
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# Read high-quality test |
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test_shape = (975, 512, 512) |
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B4X = np.memmap('test_B04.dat', dtype='int16', mode='r', shape=test_shape) |
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y = np.memmap('test_target.dat', dtype='int8', mode='r', shape=test_shape) |
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``` |
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<br> |
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This work has been partially supported by the Spanish Ministry of Science and Innovation project |
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PID2019-109026RB-I00 (MINECO-ERDF) and the Austrian Space Applications Programme within the |
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**[SemantiX project](https://austria-in-space.at/en/projects/2019/semantix.php)**. |
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