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Coastal video monitoring has proven to be a valuable ground-based technique to investigate ocean processes. Presently, there is a growing need for automatic, technically efficient, and inexpensive solutions for image processing. Moreover, beach and coastal water quality problems are becoming significant and need attention. This study employs a methodological approach to exploit low-cost smartphone-based images for coastal image classification. The objective of this paper is to present a methodology useful for supervised classification for image semantic segmentation and its application for the development of an automatic warning system for Sargassum algae detection and monitoring.A pixel-wise convolutional neural network (CNN) has demonstrated optimal performance in the classification of natural images by using abstracted deep features. Conventional CNNs demand a great deal of resources in terms of processing time and disk space. Therefore, CNN classification with superpixels has recently become a field of interest. In this work, a CNN-based deep learning framework is proposed that combines sticky-edge adhesive superpixels. The results indicate that a cheap camera-based video monitoring system is a suitable data source for coastal image classification, with optimal accuracy in the range between 75% and 96%. Furthermore, an application of the method for an ongoing case study related to Sargassum monitoring in the French Antilles proved to be very effective for developing a warning system, aiming at evaluating floating algae and algae that had washed ashore, supporting municipalities in beach management.

A new experimental campaign on a 2D movable-bed physical model, reproducing a typical nourishment sandy beach profile, is being carried out in the wave flume of the Laboratory of Coastal Engineering at Politecnico di Bari (Bari, Italy). The main aim is to assess the short-term evolution of a sandy beach nourishment, relying on a mixed solution built on the deployment of a Beach Drainage System (BDS) and a rubble-mound detached submerged breakwater. This paper aims at illustrating the experimental findings. Tests presented herein deal with both unprotected and protected configurations, focusing on the hydrodynamic and morphodynamic processes under erosive conditions. Results show that, with respect to the unprotected conditions, BDS reduces the shoreline retreat and the beach steepen within swash and surf zone as well. Moreover, a reduction of net sediment transport rate is observed. When BDS is coupled with the submerged sill, a reversal of the prevalent direction of the net sediment transport seaward occurs offshore the sheltered region. Less considerable positive effects on shoreline retreat are induced by the submerged structure, whereas the mean beach slope remains quite stable. Secondary effects of drain on the submerged sill performance are also highlighted. BDS reduces wave-induced setup on beach, by mitigating the mean water level raising, typically experienced by such structures.

Large-scale physical experiments (Froude scale, 1:4.3) were performed at the new Delta Flume in 2017, aimed at investigating wave impacts on a vertical wall placed on the top of a dike in a mild slope shallow foreshore. Experiments also allowed to investigate the morphological evolution of the sandy foreshore, the scour at the dike toe and its development under irregular and bi-chromatic wave conditions. Both experimental results and numerical study performed to design the experiments are reported. Moreover, preliminary validation of the model to investigate the scour within a wider range of wave conditions and foreshore slopes is illustrated.