Semantic Segmentation for Coastal Monitoring: Region Extraction and Overtopping Detection

Abstract

This work presents a method for analyzing coastal areas to extract regions of interest and identify significant events near the shore, using semantic segmentation adapted to these environments. The segmentation approach is applied to label all pixels in an image according to a predefined set of classes. Two additional classes—namely, foam and wet sand—are introduced to the typical categories used in coastal dynamics, allowing for more detailed differentiation of areas that are important for specific purposes. The resulting classifications are then analyzed, either individually or as a sequence of frames in a video, to detect the occurrence of relevant events, such as waves overtopping dikes and reaching pedestrian or vehicle areas, or to extract regions of interest, such as the intertidal zone. In particular, detecting overtopping involves selecting a critical region and monitoring when it is reached by the sea. On the other hand, extracting the intertidal zone implies processing sequences spanning several hours to track the sea’s temporal changes. With this approach and the additional classes, the proposed method enables more robust detection of overtopping events and more accurate delineation of the region between high and low tides.