A range of computer science methods termed machine learning (ML) enables the extraction
of insight and quantitative relationships from multidimensional datasets. Here, we review the …

This study provides an extensive review of over 200 journal papers focusing on Machine
Learning (ML) algorithms' use for promoting a sustainable management of the marine and …

The amount of marine litter, mainly composed by plastic materials, has become a global
environmental issue in coastal environments. Traditional monitoring programs are based on …

Shoreline change information is critical for effective management of the coastal zone. This
study presents a low-cost method for map** shoreline change that harnesses smartphone …

The application of deep learning, specifically deep convolutional neural networks (DCNNs),
to the classification of remotely-sensed imagery of natural landscapes has the potential to …

Coastal areas are quite fragile landscapes as they are among the most vulnerable to climate
change and natural hazards. Coastline map** and change detection are essential for safe …

Coastlines play a crucial role in coastal dynamics, and classifying their shape is an essential
requirement for coastal analysis. With the development of Coastal Management Systems …

This paper describes a novel image processing technique that detects wave breaking and
tracks waves in the surf zone using machine learning procedures. Using time-space images …

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 …

Nearshore morphology is a key driver in wave breaking and the resulting nearshore
circulation, recreational safety, and nutrient dispersion. Morphology persists within the …