Cloud Segmentation

Cloud segmentation in remote sensing aims to automatically identify and delineate cloud regions within satellite or aerial imagery, crucial for improving the accuracy of Earth observation data analysis. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and transformer architectures, often incorporating techniques like attention mechanisms and adaptive labeling to enhance accuracy and efficiency. These advancements are improving the quality of various applications, including weather forecasting, climate modeling, and resource management, by mitigating the interference of clouds in image analysis. Benchmarking studies are also contributing to the field by comparing the performance of different algorithms across various datasets and sensor types.