## 3 Results
### Literature Analysis and Main Findings
Building on the outputs of the previous work, [PERSON] and [PERSON] (2022) [97] assessed the accuracy of three machine learning classifier groups (Support Vector Machines, Multilayer Perceptron (MLP), and Ensemble Learning classifiers) on different coastal types for coastline extraction. Four kernel functions and four activation functions were tested for SVM, as well as for the MLP and EL classifiers, respectively. For the evaluation, manually extracted coastlines were compared with the estimated ones in order to determine the accuracy of the classifiers in their predictions. The coastlines were broken down to segments according to their type. It was observed that the existence of shaded areas in the bedrock coasts confused the classifiers in their predictions, whilst the most accurate productions were provided by MLP with linear, logarithmic, and tanh activation functions. For beaches, MLP and SVM with linear kernel provided accurate and consistent results, whilst the rest of the methods were affected by the shallow water depths and suspended solids. Finally, for artificial coasts, the best results were seen from MLP classifiers. [PERSON] et al. [98] reviewed the relevant convolutional neural network architectures applied for sea-land segmentation problem, and introduced an improved novel deep convolutional neural network called DeepUNet. The architecture of the proposed network involves the concatenation of the layers in the contracting path, so as for sequential convolution layers to collect outputs with a higher accuracy based on the extracted data. The images used in the context of this analysis are rendered from Google Earth, covering the RGB part of the spectrum, but they are of unknown locations and resolutions. A promising overlap tiles strategy is also proposed, so as to predict the pixels in the border region of the image/tile, by assigning weights for overlap areas that are computed by the Gaussian function, considering the distance between current pixels and the center of the tile. However, it seems the accuracy of the segmentation task is not adequate for cases with smooth sea-land boundaries or complex structures. To address such issues, [PERSON] et al. [99] introduced a new deep learning architecture (RDU-Net) for end-to-end pixel wise segmentation that utilizes convolution layers and multiscale densely connected residual network blocks in each layer of the network. The model was evaluated along with other traditional machine learning, as well as deep learning models (including the DeepUNet), showcasing an improved performance of its ability to extract deep features and to hierarchically reuse them to achieve accurate end-to-end image segmentation. Another deep learning-based sea-land segmentation model called Res-UNet was introduced in [100]. The approach relies on the U-Net architecture, replacing its contraction part with ResNet [110]. The network prediction labels are further post-processed through a fully connected Conditional Random Field image processing technique, whilst a morphological operation is also applied to further reduce isolated/scatter pixels in the label images. One of the key challenges being faced by the abovementioned approaches involves the loss of boundary accuracy during the down-sampling operation of the encoder of the model, and subsequently, the lack of a reverse process (i.e., reconstruction) in the decoder, which prevents the accurate identification of the position of the water/land boundary. Aiming to improve the positional accuracy of the coastline extraction task, [PERSON] et al. (2021) [101] proposed a multi-task network called BS-Net that adds a learning boundary coastline positioning stream to understand the exact position of the coastline, along with the segmentation network. The overall approach lies on the fusion of the water/land semantic features with the boundary features. The research work compared this approach with other thresholding methods (i.e., NDWI), machine learning models (i.e., SVM), and other deep learning architectures for semantic segmentation.