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<!DOCTYPE html>
<!--
Copyright 2021 CloudSEN12 team
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
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<link rel=stylesheet href="style.css">
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<title>CloudSEN12: A Benchmark Dataset for Cloud Semantic Understanding</title> fa-download
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<span class=title href=index.html>CloudSEN12</span>
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<a href=download.html>
<i class="fa fa-download" aria-hidden="true"></i> Download
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<a href=examples.html>
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<div class=alt-content>
<br>
<div class="textblock" align="center">
<h1>CloudSEN12</h1>
<h4>A Benchmark Dataset for Cloud Semantic Understanding</h4>
</div>
<div class="textblock">
<div class="pc">
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<p>
CloudSEN12 is a <b>LARGE dataset</b> (~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.
</p>
<p>
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: <a href="https://eartharxiv.org/repository/view/3615/" class="ref">CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2</a>.
<p>
<p align="center">Ready to start using <a href="examples.html" class="ref">CloudSEN12</a>?
</p>
<p class=centered>
<a href="download.html">
<button class="button">Download Dataset</button>
</a>
<a href="https://www.nature.com/articles/s41597-022-01878-2">
<button class="button">Paper - Scientific Data</button>
</a>
<a href="https://colab.research.google.com/github/cloudsen12/examples/blob/master/example02.ipynb">
<button class="button">Inference on a new S2 image</button>
</a>
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<button class="button">Trained models</button>
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<p class=centered>
<a href="https://gee-community-catalog.org/projects/cloudsen12/">
<button class="button">CloudSEN12 in Google Earth Engine</button>
</a>
</p>
</div>
</div>
</div>
<div class="textblock" style="font-size:xx-small">
The CloudSEN12 dataset and the pre-trained models are released under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank"> CC BY-NC-SA 4.0</a> licence. For using the models in comercial pipelines written consent by the authors must be provided.<br>
This work has been partially supported by the Spanish Ministry of Science and Innovation project PID2019-109026RB-I00 (MINECO-ERDF MCIN/AEI/10.13039/501100011033) and
the Austrian Space Applications Programme within the
<a href="https://austria-in-space.at/en/projects/2019/semantix.php" class="ref">SemantiX project</a>.
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