Byungho Kang, Orencio Duran Vinent 

Coastal erosion due to extreme events can cause significant damage to coastal communities and deplete beaches. Post-storm beach recovery is a crucial natural process that rebuilds coastal morphology and reintroduces eroded sediment to the subaerial beach. However, monitoring the beach recovery, which occurs at various spatiotemporal scales, presents a significant challenge. This is due to, firstly, the complex interplay between factors such as storm-induced erosion, sediment availability, local topography, and wave and wind-driven sand transport; secondly, the complex morphology of coastal areas, where water, sand, debris and vegetation co-exists dynamically; and, finally, the challenging weather conditions affecting the long-term small-scale data acquisition needed to monitor the recovery process. This complexity hinders our understanding and effective management of coastal vulnerability and resilience. In this study, we apply Convolutional Neural Networks (CNN)-based semantic segmentation to high-resolution complex beach imagery. This model efficiently distinguishes between various features indicative of coastal processes, including sand texture, water content, debris, and vegetation with a mean precision of 95.1% and mean Intersection of Union (IOU) of 86.7%. Furthermore, we propose a new method to quantify false positives and negatives that allows a reliable estimation of the model’s uncertainty in the absence of a ground truth to validate the model predictions. This method is particularly effective in scenarios where the boundaries between classes are not clearly defined. We also discuss how to identify blurry beach images in advance of semantic segmentation prediction, as our model is less effective at predicting this type of image. By examining how different beach regions evolve over time through time series analysis, we discovered that rare events of wind-driven (aeolian) sand transport seem to play a crucial role in promoting the vertical growth of beaches and thus driving the beach recovery process.