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NOTE: s2cloudless masks are now available as a precomputed layer within Sentinel Hub. Check theannouncement blog post andtechnical documentation.

Thes2cloudless Python package provides automated cloud detection inSentinel-2 imagery. The classification is based on asingle-scene pixel-based cloud detectordeveloped by Sentinel Hub's research team and is described in more detailin this blog.

Thes2cloudless algorithm was part of an international collaborative effort aimed at intercomparing cloud detection algorithms. The s2cloudless algorithm was validated together with 9 other algorithms on 4 different test datasets and in all cases found to be on the Pareto front. Seethe paper

The package requires a Python version >= 3.8. The package is available onthe PyPI package manager and can be installed with

$ pip install s2cloudless

To install the package manually, clone the repository and

$ pip install .

One ofs2cloudless dependencies islightgbm package. If having problems during installation, pleasecheck theLightGBM installation guide.

Before installings2cloudless onWindows, it is recommended to install packageshapely fromUnofficial Windows wheels repository

The inputs to the cloud detector are Sentinel-2 images. In particular, the cloud detector requires the following 10 Sentinel-2 band reflectances: B01, B02, B04, B05, B08, B8A, B09, B10, B11, B12, which are obtained from raw reflectance values in the following way:B_i/10000. From product baseline04.00 onward additional harmonization factors have to be applied to data according toinstructions from ESA.

You don't need to worry about any of this, if you are using Sentinel-2 data obtained fromSentinel Hub Process API. By default, the data is already harmonized according todocumentation. The API is supported in Python withsentinelhub-py package and used withins2cloudless.CloudMaskRequest class.

A Jupyter notebook on how to use the cloud detector to produce cloud mask or cloud probability mapcan be found in theexamples folder.