In-Depth Survey of Automated Shoreline Alteration Identification Methods

• Published in: 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) pp. 61 - 67
• Main Authors: Samanta, Bidisha, Banerjee, Sriparna, Gupta, Rakesh Kumar, Moulik, Soumen, Chaudhuri, Sheli Sinha
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.03.2024
• Subjects: Automated Methods; Collaboration; DSAS; Landsat; LISS; Quickbird; Remote Sensing; Reviews; Sea measurements; Shoreline Variation; Soft sensors; Software tools; Survey; Surveys; Uncertainty; WorldView
• DOI: 10.1109/ICICV62344.2024.00016

In recent years, the automated prediction and assessment of coastline changes using advanced technologies, including deep learning networks and machine learning methods, have garnered substantial attention due to their reliability and efficiency. This research domain has become increasingly vital in the contemporary era, primarily because automated shoreline change prediction holds paramount significance for sustainable coastal development, environmental conservation, disaster risk mitigation, and consequential decision-making affecting both human communities and the natural coastal ecosystems. While some authors have proposed methods for automated shoreline change detection, it remains a relatively under explored area of research. Numerous facets of this field have yet to be fully investigated. To address this gap, our study conducts an exhaustive analysis of existing methods, encompassing various dimensions such as data sources, methodological approaches, and more. By delving deeply into this subject, our work aims to provide researchers in-depth knowledge about the present state-of-the-art and offer solutions to uncharted territories in this research domain.