New Landsat 9 Cloud Cover Assessment Validation Dataset Published
Clouds are unavoidable in space-borne optical imagery. Accurate, well-documented, and automated cloud detection algorithms are necessary to effectively leverage large collections of remotely sensed data. Manually generated cloud truth masks support the efficiency of the selected algorithms.
Along with comparative validation of various cloud assessment algorithms to identify those capable of working across multiple Landsat sensors with minimal modification, cloud truth masks are essential for validating existing Cloud Cover Assessment (CCA) algorithms and can also be used to train new cloud detection algorithms. Landsat has the most comprehensive manual truth masks of any current satellite data archive.
Landsat CCA validation datasets are comprised of satellite imagery and accompanying cloud truth masks that specify the pixels in a Landsat scene that are cloudy, clear, or have other conditions such as cloud shadow.
A new Landsat 9 based CCA validation dataset was recently published. Similar CCA data were compiled for Landsat 7 and Landsat 8 Collection 2 data. All CCA validation datasets can be found at https://www.usgs.gov/landsat-missions/cloud-cover-assessment-validation-datasets.
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