Cloud Mask Generation

Satellite images play an essential role in contemporary remote sensing and geospatial analysis, being utilized in land use/land cover classification, environmental monitoring, agricultural assessment, disaster management, change detection, object detection, and many other cases. On the other hand, the quality of satellite imagery can be seriously compromised by clouds, which hide the Earth's surface features and reduce the possibility of extracting reliable information about the studied area.

Thus, the cloud coverage should be detected in most cases and then excluded or processed using special methods for removing and correcting clouds. One of the main steps in the preprocessing of cloud-covered imagery involves creating cloud masks, which allow separating cloud-affected pixels from those observed under clear sky conditions. Nevertheless, even though cloud masks can be created manually for each particular satellite scene, this process is rather laborious and time-consuming.

In order to automate and simplify the process of cloud masking, the described model is supposed to be used for the automatic generation of cloud masks for Sentinel-2 satellite images. In such a way, this tool will help users to perform the necessary preprocessing of satellite imagery and prepare it for further analysis.

The automatic cloud masking feature using Sentinel-2 can especially be useful for institutions and research organizations dealing with large volumes of Earth observation data. This process facilitates efficient processing of images and ensures that cloud-covered pixels are detected before carrying out any analysis. Cloud masks generated from this process can then be used in various geospatial applications such as land cover mapping, vegetation monitoring, change detection, and object detection, among others.

Using satellite imagery together with automated cloud detection and masking enables users to save on processing time and build a better foundation for Earth observation.