## 3 Results
### Literature Analysis and Main Findings
two segmentation algorithms were tested (multi-resolution image segmentation and mean shift segmentation); thus, in total, six different configurations have been applied. ArcGIS software was utilized to convert the classification results to vector format, and then a smoothing process was applied so as to remove noise. The accuracy of each approach was examined through in situ measurements (GPS survey), and the differences between the shorelines were calculated via DSAS software. The study showed that a Random Forest classifier with the multi-resolution algorithm (object-based approach) yielded better results for sandy coasts. A similar case involving the application of an object-based image analysis approach has been applied by [PERSON] et al. (2018) [85] for mapping mangroves, through the integration of both optical and SAR imagery. Different ML classifiers have been employed (Random Forest, Support Vector Machine, and Classification and Regression Trees), with the highest accuracy to be observed from the combined use of optical and SAR data as input parameters, and the application of the RF algorithm. In situ data collected from field campaigns have been also used for both the training and the validation of the algorithms. The use of Random Forest, Logistic Regression, and naive Bayes tree machine learning algorithms, along with Landsat images for mangrove forest mapping and mapping, have been also investigated by [PERSON] et al. (2020) [86], whilst [PERSON] et al. (2020) [87] exploited Sentinel-2 and Worldview-2 imagery and the Random Forest algorithm for the classification land cover classes in a mangrove ecosystem. [PERSON] et al. (2018) [88] proposed a hybridization of segmentation algorithms and ML techniques for object-based image analysis for shoreline extraction. During the study, 11 single and 4 ensemble learning classifiers have been evaluated. In the context of the hybridization process (the initial application of segmentation in order to lead to improved classification results), the application of the Simple Linear Iterative Clustering segmentation algorithm along with the Extra-Tree classifier yielded the best results. Unsupervised machine learning approaches have also been investigated in the literature. [PERSON] et al. (2022) [89] applied agglomerative hierarchical clustering so as to group coastal features such as topographic metrics, soil type, and vegetation indices, and to identify zones with similar characteristics. This tree-based approach computes the distance between the different observation pairs in order to cluster them into relevant groups. [PERSON] et al. (2022) [90] investigated the applicability of K-Means and K-Nearest Neighbor (KNN) algorithms for land/sea discrimination. The approach is applied on the 10 m bands of Sentinel 2 satellite, on the Normalized Difference Water Index (NDWI), and on both of the above-mentioned inputs. It was found that both algorithms yielded good results, with the best ones to be provided from the mere use of the NDWI layer, while the unsupervised method also showcased a better performance in comparison with the KNN algorithm. An unsupervised classification approach (K-Means) for the production of the mean maps of coastline states at different time instances was tested by [PERSON] et al. (2022) [91]. The approach was based on multispectral data (Sentinel 2 and Landsat imagery) and while exploiting specific remote sensing indices (i.e., NDWI, NDVI, etc.) as part of the process of generating yearly average water probability maps. [PERSON] et al. (2022) [92] investigated coastal erosion and accretion, and shoreline changes due to climate change for a 14 year period through conventional RS and GIS techniques, whilst the generated data were used for training a machine learning model to predict the short-term coastal erosion status. A k-means clustering along with linear curve fitting was applied for the prediction of future shorelines. For the validation of the predicted values, error metrics such as Root Mean Squared Error and Mean Absolute Error were applied.