## 3 Results
### Literature Analysis and Main Findings
(2020) [80] assessed coastal geomorphological changes occurring due to coastal erosion through satellite derived bathymetry. Bathymetry data at different time instances were generated by applying a non-linear machine learning technique of Support Vector Regression while harnessing multispectral Landsat 8 imagery. The method is stated to provide good results, particularly in cases with a limited availability of input data incomparison with other techniques (i.e., Random Forest model) that need a large amount of data in order to ensure a good convergence. The model results are further confirmed with a shoreline change study performed through GIS software (Digital Shoreline Analysis System). The approach indicates positive results for shallow water depth estimation. A coastal geomorphological analysis was conducted by [PERSON] et al. (2022) [81] at a global scale, covering Australia, US, and EU. The proposed approach involved the combination of raster data with vectorized descriptors in the context of machine learning models towards the classification of intertidal coastal geomorphic features. The coastline data were derived from OpenStreetMap, which according to the authors, was of adequate accuracy in comparison to coastline extracted from Sentinel 1 data. Various geometrical, spectral, and auxiliary data were extracted from coastlines and earth observation data, as well as other existing data from coastal segments. Six supervised classification methods were evaluated in the context of the analysis, including Support Vector Machine, Neural network, Gaussian Naive Bayes, Decision Tree, Random Forest, and adaptive boosting. Coastal areas around the globe (EU, US, and Australia) were classified as bedrock, beach, and wetland. The model implementation was based on geometrical, spectral, and auxiliary variables, and principal component analysis was applied in order to reduce the geometrical dimensions. Then, each machine learning method was iterated for none to all of the geometrical variables extracted from the PCA analysis, and a balanced accuracy from five-fold cross validation was applied for the selection of the best approach (model and input parameters combinations). The Random Forest model outperformed the rest of the models, and it was the one that was applied for the global coastal classification task. The validation was performed through both the testing and validation datasets (with the latter to be provided from independent sources) achieving an overall good accuracy (85% and 84.7%, respectively). Finally, it was found that geometric variables improved the overall classification accuracy; yet uncertainties and misclassification cases were also observed, due to the lack of accuracy and proper variability of the input data. [PERSON] et al. (2016) [82] proposed a graph cut-based supervised method to segment the sea and the land from natural-colored images while clustering the image pixels into superpixels. Then, a superpixel-based Support Vector Machine model was implemented for sea-land segmentation. Given the semantic information in the satellite images, a misclassification of land-based pixels was observed (i.e., shadow and green colored regions in the land areas may be classified as water, and waves and noises in the water areas as land). A supervised learning-based approach that translates the land-sea segmentation problem to a binary classification task was illustrated by [PERSON] et al. (2017) [83]. The first part of the approach involves the extraction of pixel-wise features, including local statistics, edge, texture, and structural information from training images. Then, a multi-feature classifier is trained and utilized to perform sea-land segmentation. Different methods have been investigated by [PERSON] et al. (2021) [84] towards the identification of a reproducible shoreline extraction method. Approaches involved the assessment of both pixel-based and object-based image analysis on supervised machine learning classifiers (Support Vector Machine and Random Forest). In the context of object-based analysis.