Cloud Masking

Cloud masking, the process of identifying and removing cloud cover from satellite imagery, is crucial for accurate analysis of Earth observation data across various scientific disciplines. Current research emphasizes the development of robust and generalizable deep learning models, including multi-task learning architectures and transformer-based networks, to improve the accuracy and efficiency of cloud detection across diverse satellite sensors. These advancements aim to overcome limitations of traditional methods and enable more reliable analysis of Earth's surface features for applications such as climate modeling, environmental monitoring, and precision agriculture. The development of standardized benchmarks and datasets further facilitates collaborative research and the creation of widely applicable cloud masking solutions.