In-Depth Survey of Automated Shoreline Alteration Identification Methods

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...

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Published in2024 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
LanguageEnglish
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
Online AccessGet full text
DOI10.1109/ICICV62344.2024.00016

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Summary: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.
DOI:10.1109/ICICV62344.2024.00016