Emerging methodologies in waterbody delineation: an In-depth review

Waterbody extraction from satellite imagery plays a crucial role in various environmental monitoring and management applications. Accurate identification and delineation of water bodies are essential for assessing water resources, monitoring changes in aquatic ecosystems, and supporting decision-making processes. This review presents a comprehensive analysis of different methods used for waterbody extraction from satellite images, highlighting their strengths, limitations, and recent advancements. This review begins by discussing traditional methods, such as thresholding-based methods, machine learning methods, and object-based image analysis, which have been widely employed in the past. Consequently, the focus shifts towards, how deep learning models, such as convolutional neural networks (CNNs) have been applied to improve waterbody extraction accuracy and address challenges posed by spectral variations, cloud cover, and sensor limitations. Overall, this review serves as a valuable resource for researchers, practitioners, and decision-makers involved in water resource management and environmental monitoring.