Cloud Type Classification Through Semantic Segmentation for Analysis of Earth’s Radiative Balance

Classifying nighttime clouds is crucial for understanding their impact on Earth's radiative balance. This study presents a semantic segmentation model using U-Net with a MobileNetV3 backbone for classification of the following cloud types: Cirrus, Nimbus, Stratus, and Cumulus from nighttime images. Despite challenges from reduced visibility at night, cloud types and coverage were effectively detected, classified and measured. The model results potentially facilitate future research on nighttime radiation analysis.