Remote sensing data acquired through multi-spectral satellite sensors provide an opportunity to monitor and understand the Earth's physical, chemical, and biological systems. However, approximately 70% of the sky is often covered by clouds, which can lead to the processing and storage of images that are not useful for Earth observation tasks, such as monitoring vegetation, ice, or water bodies. To better understand which images should be processed and how, cloud detection algorithms have been designed to identify cloudy pixels, generating cloud segmentation masks that can be used to compute cloud coverage. Accurate detection is essential to filter out highly cloud-covered images for reliable downstream processing.