Cloud Cover Segmentation and Motion Prediction from Satellite Imagery

Abstract

Abstract: This research project is dedicated to achieving precise identification, segmentation, and motion prediction of cloud formations in satellite imagery. Leveraging the powerful U-Net, a renowned deep learning architecture for image segmentation, is crucial in addressing the intricate challenge of cloud detection and segmentation within remote sensing imagery. The automation of cloud identification processes within the project not only enhances weather forecasting capabilities but also contributes significantly to advancements in climate monitoring and environmental analysis. The incorporation of Long ShortTerm Memory (LSTM) networks facilitates cloud motion prediction, providing insights into the dynamic behavior of cloud formations over time. The robustness of the U-Net model, coupled with the enhanced capability of capturing intricate patterns and predicting cloud motion, establishes it as a valuable and comprehensive tool. This contributes to improving the accuracy and efficiency of cloud segmentation in satellite imagery, fostering progress in critical domains such as environmental research and meteorological applications