###### Abstract  
Climate change and its effects (i.e., sea level rise, extreme weather events) as well as anthropogenic activities, determine pressures to the coastal environments and contribute to shoreline retreat and coastal erosion phenomena. Coastal zones are dynamic and complex environments consisting of heterogeneous and different geomorphological features, while exhibiting different scales and spectral responses. Thus, the monitoring of changes in the coastal land classes and the extraction of coastlines/shorelines can be a challenging task. Earth Observation data and the application of spatiotemporal analysis methods can facilitate shoreline change analysis and detection. Apart from remote sensing methods, the advent of machine learning-based techniques presents an emerging trend, being capable of supporting the monitoring and modeling of coastal ecosystems at large scales. In this context, this study aims to provide a review of the relevant literature falling within the period of 2015-2022, where different machine learning approaches were applied for cases of coast-line/shoreline extraction and change analysis, and/or coastal dynamic monitoring. Particular emphasis is given on the analysis of the selected studies, including details about their performances, as well as their advantages and weaknesses, and information about the different environmental data employed. machine learning; geospatial intelligence; earth observation; coastal erosion; shoreline extraction; literature review +
Footnote †: journal:  
last swash) [6; 7], the wet/dry line (i.e., the boundary of the dimmer sand) [8; 9] and water line defined as the interface between the water and the land [10; 11; 12], morphological limits (i.e., cliffs [13; 14; 15], dunes [16; 17; 18; 19], headwall scarps, or protected seafront structures [20; 21]) and/or vegetation limits [22; 23; 24; 25]. On the other hand, tidal-datum indicators such as mean high/low water line [26; 27; 28] or mean sea level [29] are defined based on vertical profiles shaped by the rise and the fall of the tides in association with the coastal profile, whilst meteo-marine insights to relevant stakeholders can be also provided, calculating the wave flow and height, tide phases, or other extreme events (i.e., storm surges) [30]. During the past few years, different tools and approaches have been used in the service of coastal erosion and coastline change monitoring. The majority of these tools rely on geographic information systems (GIS), including both close-source (i.e., ESR ArcGIS and Digital Shoreline Analysis System [31]) and open-source software (i.e., QGIS and Open Digital Shoreline Analysis System [32]), allowing users to manage and to generate all the geospatial related information that can be subsequently utilized in the context of other programming environments [32]. In the last decade, approaches regarding shoreline change analysis and detection are oriented towards the spatiotemporal analysis of satellite images of high resolution. Apart from remote sensing methods, the advent of machine learning and artificial intelligence-based techniques presents an emerging trend, being capable of supporting the automated extraction of shorelines at large scales. Such methods can facilitate the analysis of large amounts of complex environmental data, and the identification of patterns and trends that are difficult to discern with conventional means, as well as support the generation of accurate habitat maps and the development of predictive models of how coastal ecosystems will respond to different environmental pressures [5]. However, in comparison to other land applications, a relatively small number of studies are focused on the marine domain, and in particular, to tasks related to coastline detection, mainly due to the diverse coastline morphological features that need to be considered, especially at large-scale monitoring scenarios [33].