GENERATIVE AUGMENTATION FOR SKY/CLOUD IMAGE SEGMENTATION

Cloud image segmentation plays a pivotal role in fields such as weather prediction, climate modeling, and renewable energy systems. Although ground-based sky imagers are preferred tools for cloud image analysis, their images present unique challenges for segmention due to noise, sun glare, and other factors. Recent success in cloud image segmentation is attributed to the use of deep learning techniques. However, they require large annotated datasets for improved performance and robustness. This paper(1) introduces a two-step generative framework that simultaneously generates sky/cloud images and their corresponding ground-truth segmentation maps to augment the dataset and demonstrates the performance of the proposed approach over two prominent semantic image segmentation models and sky/cloud patch image datasets.