A Taxonomy of Sky Region Detection: Challenges and Solutions

Detection of sky regions is one of the most crucial challenges in image processing and computer vision applications, including scene parsing, picture retrieval, weather forecasting, and robot navigation. However, it is challenging to detect sky regions under certain circumstances, particularly in gloomy and overcast conditions. This study aims to summarize sky region detection approaches, challenges, and applications together. Additionally, classical, and deep learning-based approaches have been delineated. An extensive literature review has been conducted to achieve the objectives of the study. It has emerged that various machine and deep learning approaches have been proposed. Unfortunately, most of the approaches lose efficacy when encountering overcasting or lighting conditions, as most of the approaches are trained on an ideal dataset. Moreover, a taxonomy of sky region detection challenges has been proposed, categorizing the identified challenges into edge-based, color-based, texture-based, deep learning-based methods, etc. The challenging datasets that are being utilized for robust sky detection methods have been presented.