Deep Learning-Based Semantic Segmentation for Automatic Shoreline Extraction in Coastal Video Monitoring Systems

Dynamic and vulnerable, coastal zones face multiple hazards such as storms, flooding, and erosion, posing serious risks to populations and ecosystems. Continuous observation of coastal processes, particularly shoreline evolution, is therefore essential. Over the past three decades, coastal video-monitoring systems have proven valuable and cost-effective for studying coastal dynamics. Several approaches have been proposed to determine shoreline position, but each presents limitations, often depending on local conditions or illumination. This study proposes a method based on semantic segmentation using deep neural networks, specifically U-Net and DeepLabv3+ architectures. Both models were trained using time-exposure images from a coastal video-monitoring system, with DeepLabv3+ further evaluated using four convolutional neural network (CNN) backbones (ResNet-18, ResNet-50, MobileNetV2, and Xception). Unlike previous satellite- or UAV-based studies, this work applies deep learning to fixed coastal video systems, enabling continuous and high-frequency shoreline monitoring. Both architectures achieved high performance, with Global Accuracy of 0.98, Mean IoU between 0.95 and 0.97, and Mean Boundary F1 Score up to 0.99. These findings highlight the effectiveness and flexibility of the proposed approach, which provides a robust, transferable, and easily deployable solution for diverse coastal settings.