Advanced AI Strategies for Coastal Analysis: A Case Study at Las Canteras Beach in Gran Canaria

Abstract

We propose a semantic segmentation model to analyze coastal dynamics. Using advanced AI techniques, precise segmentation masks are generated, overcoming challenges like changing weather conditions, glare, or shadows. A diverse dataset ensures adaptability, classifying features such as waves, sand, foam, and static infrastructures at the pixel level. This enables detailed analysis of interactions between marine elements and coastal structures, and can lead to measurements such as wave period, crucial for predicting overtopping events and identifying abnormal sea behavior. Experiments at Las Canteras Beach in Gran Canaria, a location where our model was not trained, yet it still performed well, demonstrate its effectiveness. This research illustrates AI's potential in advancing coastal management and environmental monitoring.