## 2 Data and Methods
### Search Strategy  
As part of this systematic literature review procedure, a search of peer-reviewed journals took place in the electronic literature databases of Scopus and Web of Science (WOS), while excluding the grey literature (conference papers, presentations, commentary, extended abstracts, etc.). Boolean operators combining multiple keywords prominent to the research topic, are presented in Table 1 and were queried in the aforementioned database. A total number of 78 papers were retrieved and evaluated in terms of their relevance with the scope of this study. The above-mentioned figure is in line with the bibliometric analysis performed within 2022 by [PERSON] et al. [5], indicating that approximately 5.1% from a total number of 1578 articles that were examined, involve the use of machine learning tools for shoreline change analysis, whilst only 57 papers were published from 2013 and until the first trimester of 2022. Out of this number, 36 papers and studies were selected and analyzed, covering the period between 2015 and 2022 (Table 2). The selection of the papers was conducted based on their relevance, aiming to cover different cases of coastline/shoreline extraction and change analysis, and/or coastal dynamics monitoring. \begin{table}
\begin{tabular}{l c} \hline \hline
**Paper/Study Title** & **Authors** \\ \hline Coastline detection in satellite imagery: A deep learning approach on new [PERSON] et al. (2022) [33] \\ benchmark data & \\ \hline Multispectral satellite imagery and machine learning for the extraction of & [PERSON] et al. (2022) [34] \\ shoreline indicators & \\ \hline Evaluating the impacts of major cyclonic catastrophes in coastal Bangladesh using [PERSON] et al. (2021) [74] \\ geospatial techniques & \\ \hline Spatial–Temporal Land Loss Modeling and Simulation in a Vulnerable Coast: A Case & [PERSON] et al. (2022) [75] \\ Study in Coastal Louisiana & \\ \hline Leveraging the Historical Landsat Catalog for a Remote Sensing Model of Wetland & [PERSON] et al. (2022) [76] \\ Accretion in Coastal Louisiana & \\ \hline Remote sensing and GIS analysis for mapping spatio-temporal changes of erosion and & \\ deposition of two Mediterranean river deltas: The case of the Axios and Aliakmonas & [PERSON] et al. (2015) [77] \\ rivers, Greece & \\ \hline Coastal erosion detection using Landsat satellite imagery and support vector & [PERSON] and [PERSON] (2022) [78] \\ machine algorithm & \\ \hline Shoreline extraction from WorldView2 satellite data in the presence of foam pixels using & [PERSON] et al. (2020) [79] \\ a multispectral classification method & \\ \hline Assessment of coastal geomorphological changes using multi-temporal & [PERSON] and [PERSON] (2020) [80] \\ Satellite-Derived Bathymetry & \\ \hline Global coastal geomorphology—integrating earth observation and geospatial data & [PERSON] et al. (2022) [81] \\ \hline Efficient sea-land segmentation using seeds learning and edge directed graph cut & [PERSON] et al. (2016) [82] \\ \hline Multi-feature sea–land segmentation based on pixel-wise learning for optical & [PERSON] et al. (2017) [83] \\ remote-sensing imagery & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of selected papers/studies considered in the context of the study.