Lake Extraction

Lake extraction from remote sensing imagery focuses on accurately identifying and mapping lakes globally, crucial for monitoring their dynamics and understanding their ecological roles in a changing climate. Recent research emphasizes improving the temporal and spatial resolution of lake datasets, employing advanced deep learning architectures like Swin-Unet, CNN-Transformer hybrids (e.g., LEFormer), and prompt-based methods to enhance segmentation accuracy and efficiency. These advancements are driven by the need for more precise and timely information on lake area changes, informing studies of hydrological processes, climate impacts, and ecosystem services. The development of robust and efficient lake extraction techniques is vital for effective environmental monitoring and management.