Coastline Detection

Coastline detection aims to automatically identify the boundary between land and water in satellite imagery, crucial for monitoring coastal changes like erosion and human impact. Current research focuses on improving accuracy using deep learning models, such as U-Net, and exploring alternative approaches like edge detection algorithms (Canny, Sobel) and superpixel segmentation (MISP-GGD), often enhanced by pre-processing techniques. The effectiveness of various evaluation metrics is also under scrutiny, with a need for robust methods beyond visual assessment. Improved automated coastline detection offers significant benefits for coastal management, environmental monitoring, and resource planning.