## 3 Results
### Literature Analysis and Main Findings
Other methods focus on edge detection and optimization procedures towards the automated coastline extraction. [PERSON] et al. (2018) [93] provide an automated methodology that uses aerial images through image processing techniques aimed at region segmentation and edge detection. The initial step involves the application of an anisotropic diffusion algorithm, so as to reduce the noise and to enhance the edges of the image. Then, image segmentation is applied, splitting the image into two regions (land and water) using suitable thresholds that are derived from local area characteristics. As a final step, and due to the fact that objects near the coastline affect the result, an active contour method is adopted, allowing for the identification of deformations to occur using such physical objects. The processes of thresholding, edge detection, and active contour-fitting improved the accuracy of the extracted coastline. In order to address the limitations observed by thresholding approaches that focus only on the spectral information of each image pixel without considering the neighboring ones, as well as the challenges observed in the cases of images with complex semantic information (i.e., a dependence on manually selected features and a high rate of misclassified pixels), deep learning-based methodologies have been investigated for coastline monitoring applications. [PERSON] et al. (2021) [94] evaluated the use of remote sensing indices, machine learning models (Random Forest and eXtreme Gradient Boosting) and a deep learning approach (a modified U-Net architecture), along with very-high-resolution spaceborne imagery for coastline mapping in the Arctic. The introduced framework is focused on a water/land semantic segmentation (pixel-wise classification) approach, being able to use sparsely labeled data for generating a dense grid of segmented labels, and thus addressing the cases of limited data availability. Despite providing good results, the deep learning-based U-Net model is slightly outperformed by single pixel models such as Random Forest. According to the authors, this might be attributed to training with sparsely labeled data, as the neural network architecture might not be able to exploit the full spatial properties of the model. In other works, the utilization of deep learning methods with an increased number of land cover classes showcased better results [108]. Aiming to identify shoreline changes, [PERSON] et al. (2018) [95] also utilized supervised machine learning techniques (Multilayer Perceptron Artificial Neural Network, K-Nearest Neighbor, and Support Vector Machine) for the classification of satellite images (Landsat and SPOT-5) in land and in water. Then, the boundaries of the abovementioned classes were located and properly processed (the smoothing process), so as to enable the proper extraction of the shoreline, since some issues were observed between the classification accuracy and the quality of the derived shoreline [109]. Data validation was achieved via the available reference shoreline data, whilst the Multilayer Perceptron Artificial Neural Network model was the most effective one. Finally, the GIS environment was selected for the calculation of the rate of change statistics for the available timeseries shoreline data; yet, the selection of only two classes for the classification prevents the elicitation of more fine-grained information regarding the occurrence of coastal erosion. A similar study [96] was conducted by the authors, that used pixel-based approaches to classify land-water classes using the majority voting of ensemble and single classifiers. Overall, 11 single classifiers and 4 ensemble classifiers were tested, with MLP to demonstrate the highest accuracy, whilst the combination of ensemble classifiers (Random Forest and SVM) using majority voting algorithm was proven to be the most effective method.