Matej Batič
Jan 22, 2018 · 2 min read

Sentinel Hub Cloud Detector — s2cloudless

With the sentinelhub-py library out in the open, we are happy to add another Python tool to help you untangle the value from the EO data: Sentinel Hub cloud detector for Sentinel-2 data.

Acatenango area in Guatemala is well known for its coffee plantations. At the altitute about 2000 m and given it’s climate, it is often veiled in clouds. Middle image is natural color image, binary cloud mask is on the left, and cloud probability map is on the right.

In previous blog post about the clouds and Sentinel-2 data, Anze Zupanc already discussed the problems of cloud detection, and presented a solution to the problem. As we believe that the best way to (stress) test the algorithm is to expose it to as many expert eyes as possible, we are publishing the package containing the classification model and some helper classes to get you going: s2cloudless.

Some more examples, overlayed with semi-transparent cloud mask.

The package provides an automated cloud detection for Sentinel-2 imagery, and the classifier is based on a single-scene pixel-based cloud detector developed by Sentinel Hub’s research team, as described in more details here.

Enjoy hunting down the ☁️!

Jupyter notebook in the examples will walk you through the procedure to get from the bounding box and time to the cloud mask.

Sentinel Hub Blog

Stories from the next generation satellite imagery platform

Thanks to Anze Zupanc and Devis Peressutti

Matej Batič

Written by

A bit of a coffee addict. Tackling EO.

Sentinel Hub Blog

Stories from the next generation satellite imagery platform

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