OmniCloudMask
OmniCloudMask is a Python library designed for state-of-the-art cloud and cloud shadow segmentation in high to moderate resolution satellite imagery. It offers higher accuracy and supports a wide range of resolutions, sensors, and processing levels, validated on data from Sentinel-2, PlanetScope, Landsat, and Maxar.
OmniCloudMask is a Python library for state-of-the-art cloud and cloud shadow segmentation in high to moderate resolution satellite imagery. As a successor to the CloudS2Mask library, it provides enhanced accuracy while supporting a diverse range of resolutions, sensors, and processing levels.
This library has been extensively validated on Sentinel-2, PlanetScope, and Landsat data, and is also known to perform effectively with Maxar data. It is designed to work with any imagery featuring Red, Green, and NIR bands, provided a spatial resolution of 50 meters or better.
Further resources include the OmniCloudMask paper, a training data distribution map, and a podcast episode discussing the library. Detailed version history and model notes are available in the respective changelogs.
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