Machine Learning techniques for the detection of geomorphological features in nearshore environments

Marine geophysical surveys provide crucial data and information for monitoring purposes and engineering application support on coastal and marine environments. Habitats associated to these specific natural contexts represent highly sensitive ecosystems that have been constantly threatened by human activities over the past few decades. Indeed, as stated by the European Commission, the 79% of the European coastal seabed is disturbed due to bottom trawling. Moreover, due to the ever-increasing demand of food and resources from the sea, issues as pollution, biodiversity loss, seabed damage, the spread of non-indigenous species, and similar phenomena are ever more serious. For this reason, the Marine Strategy Framework Directive (MSFD) were defined in 2008 by the European Commission to protect and keep safe its coasts, seas, and the ocean, ensuring their sustainable use. To this aim, marine geophysical techniques provide valuable tools for the assessment of biocenosis health status and distribution on a large scale. On the other hand, also engineering and industrial applications, such as offshore renewable energy production, onshore facilities, pipe installations or harbour maintenance, require high-resolution bathymetrical and sea-floor data for safe and sustainable operations, only obtainable with geophysical surveys.

Concerning the nearshore environment investigation, standard marine survey techniques used so far consist of methodologies exploiting the propagation of acoustic waves in the water column, i.e., Side Scan Sonar (SSS), Single and Multi-beam Echo Sounder (SBES/MBES) and Sub-bottom Profiler (SBP). Moreover, camera acquisitions and sub-marine stereo-photogrammetry are increasingly used for the analysis of seafloor morphology, although limited to optimal water conditions. Recently, thanks to the AI techniques improvements, Machine Learning (ML) techniques, coupled with GIS software, represent valuable tools for interpreting and mapping sub-merged morphological features on geophysical data using a multidisciplinary approach.

In this context, our research proposes a Computer Vision implementation using Convolutional Neural Networks (CNNs) for the detection and classification of marine morphological features in nearshore sectors of the Italian coastal environment.  Two different CNNs algorithms were used for the automatic segmentation and classification considering one considering the most marine morphological features of the study area and recognizable on SSS orthomosaics. The latter were acquired in two coastal sites of the Apulia Region (Southern Italy): Torre Guaceto Beach (Brindisi), on the Adriatic coast, and Leporano beach (Taranto) on the Ionian seaside. The first CNN algorithm is U-Net while the second one is a Mask-RCNN-based algorithm, already used in previous works to detect Beah Litter items on the emerged section of a beach. The training datasets were suitably processed to make them available for both algorithms, which process data in a slightly different way. Moreover, the training dataset based on the nearshore environment of the Apulian coastal sector will make it possible to map seabeds with similar morphological characteristics. This multidisciplinary approach represents an early stage of a first and promising integration tool to the classical manual image screening of marine seafloor morphology on a large homogeneous seabed, characterizing most of the Mediterranean coasts. Further development will concern additional geophysical surveys that will increase the dataset for a higher detection accuracy.