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.

A new coastal video monitoring system for Apulia region, southeast of Italy, is under development. It is composed of visible and thermal streams and featured to be included in a wider meteo-oceanographic monitoring network. The system is designed for use on sandy beaches. The regional Basin Authority (AdBP) through previous field campaigns has identified for long-term monitoring purposes two hotspots, prone to erosion and flooding: Torre Canne (Fasano, BR) and Torre Lapillo (Porto Cesareo, LE), facing the Adriatic and Ionian seas, respectively. This paper presents the actual architecture and some initial findings of its implementation aiming at a complete automatic analysis of morphological features and hydrodynamic studies, mainly focused in swash zone.

Valentini, N.; Balouin, Y., and Bouvier, C., 2020. Exploiting the capabilities of Surfcam for coastal morphodynamic analysis. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1333–1338. Seville (Spain), ISSN 0749-0208.A free webcam network is used in this study, operated by ©Viewsurf, a European company with more than 1200 stations in France, among beaches, urban and mountain areas in order to assess the capabilities of surfcam for coastal morphodynamics analysis. Four representative coastal sites, with different tidal range and wave energy are monitored in order to extract optical signatures for morphodynamic analysis: three in the Atlantic and a gravel beach in the Mediterranean area. A representative site is here documented, Hendaye beach, which is characterized by a meso-tidal environment, with highly energetic waves often present, which makes the image processing challenging. Shoreline detections for beach width and intertidal area calculation are accomplished by using a new image segmentation algorithm, based on a framework, derived by opportunely coupling a superpixels algorithm and a Deep-Conditional-Neural-Network DCNN model. Aiming to exploit the capabilities of cBathy algorithm (Holman, Plant, and Holland, 2013), a version of the open-source methodology (https://coastal-imaging-research-network.github.io), is employed for bathymetry reconstruction. A coupled bathymetry by merging cBathy depth estimate and intertidal reconstructed depth from automatically extracted shorelines is accomplished, which is compared with survey and lead to an RMS error of 0.43 m. Discussions are given with respect to this new potential open-source resource and its limitations.