## 4 Discussion  
Coastal zones are dynamic and complex environments consisting of heterogeneous and diverse 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-based approaches for shoreline extraction should investigate the relationships between scene-dependent variables and shoreline accuracies, so as to better understand coast-scale performance variations. The availability and quality of other physical parameters such as waves, tides, and beach slope can influence the shoreline detection task and the accuracy of the methods. Approaches relying on water indexes are quite common in the studies for monitoring shoreline/coastline extraction, and subsequent changes due to events. However, the use of single band images and the application of thresholding techniques can lead to controversial results when it comes to continuous monitoring applications. This is due to the fact that each new image consists of different illumination conditions, and thus requires continuous adjustments of the threshold, so as to lead to stable results. The application of normalized or standardized surface reflectance could address such issues. Neural network-driven approaches should take into account and be trained so as to discriminate between water and land at different tidal phases. Additionally, studies have shown that increasing the input size of the machine learning model could lead to more spatial and attribute details about coastline/shorelines, facilitating land-water discrimination, as well as the detection of space between them. Moreover, recent studies employing more complex architectures (i.e., neural networks) mainly exploit RGB bands. The use of additional spectral bands could also be useful in obtaining different results, whilst the use of indexes (i.e., NDWI) apart from the spectral bands can lead to improved model results. In cases where object-based analysis is used as the basis of coastal classification, different segmentation approaches could provide different classification results. It seems  
Figure 3: Schematic representation of the machine learning techniques investigated in the literature for shoreline/coastline extraction and monitoring of coastal dynamics [33; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 107]. also that pixel-based approaches are more sensitive to coastline characteristics and heterogeneities (i.e., dunes, vegetation, etc.) compared to object-based approaches, in which apart from spectral information, other properties (i.e., shape, texture, etc.) can be considered as well. However, in the context of object-based segmentation approaches, it might be difficult to select the appropriate scale when it comes to large-scale studies, and thus, subsequent steps such as classification might be required. Deep learning introduces new innovative concepts for semantic segmentation and classification tasks. Deep learning architectures have good generalization and could support different scenarios of land/water segmentation towards the extraction of shorelines. Current research trends emphasize on addressing cases of complex shorelines in terms of their sizes, shapes, and compositions. Differences in the reflectances of the different coastal elements can facilitate the extraction of coastlines. However, the existence of shaded/unshaded areas can lead to discrepancies in the characterization. The existence of clouds (particularly in the countries of the northern hemisphere) could further hinder characterization. The combination of approaches relying both on optical and SAR data could be useful in addressing such cases. Very-high-resolution data can also lead to more accurate results in the shoreline extraction process. However, these data are usually costly and thus cannot be easily applied for large-scale monitoring.