## 3 Results
### Literature Analysis and Main Findings
The approach could lead to improved results in comparison with other methods, while also exhibiting a high consistency between the extracted water/land boundaries and the actual coastlines. Further to that, [PERSON] et al. (2021) [102] attempted to improve the classic encoder-decoder structure and propose a deep learning model called SANet for sea-land segmentation that is based on adaptive multiscale feature learning. The approach allows for the extraction of multiscale detailed information and the contextual semantic information of objects that can subsequently and adaptively fuse feature maps of different scales. [PERSON] et al. (2022) [33] presented a labeled image dataset suitable for the automated extraction of coastline morphological features from Copernicus Sentinel 2 imagery. The dataset aims to address the current lack of openly available resources that prevent the benchmarking of machine learning models and cross applications comparisons and evaluation, while enabling an enhanced understanding of how model performance metrics relate to the spatial qualities of the extracted geomorphological features. The study employs different convolutional neural network models in order to detect coastline morphology and to provide a baseline performance against the dataset. It was demonstrated that the U-Net model optimized using the Sobel-edge loss function led to an improvement of image segmentation tasks for shoreline detection, in comparison to more commonly used loss functions. [PERSON] et al. (2022) [103] proposed a system relying on indicators that was utilized as the basis for the computation of deep learning models towards the identification of coastlines and shorelines. Considering the fact that different model structures can lead to different performances, four structures were tested, including UNet, UNet3+ (applying a connection between encoders-decoders, as well as a connection between decoders, allowing for the elicitation of detailed information and refined semantics from full scale data), U2-Net (allowing for the acquisition of deeper connections and with higher resolution, and while employing a set of encoders, a set of decoders, and a fusion module linking encoder-decoder stages) and DexiNed (facilitating the detection of end-to-end characteristics computed in shallow layers). The scope of the application of the above-mentioned models was also made in order to allow for the assessment of the rate of erosion/accretion. The U-Net model with an image input size of 512 \(\times\) 512 yielded the highest accuracy, enabling the effective identification of coastlines and shorelines towards assessing coastal erosion due to sea level rise. A validation of Sentinel-2-based subpixel instantaneous shoreline extraction was performed by [PERSON] et al. (2022) [104], with data being collected from drone surveys and other in situ measurements. For the shoreline extraction, different methods were evaluated, including water indices, CoastSat [105], and a tidal-balanced convolutional neural network, whilst the approach considers each satellite image as a single point instead of image composites. The latter is applied in order to ensure the availability of an adequate number of images to support the short-term quantification of shoreline changes, and thus capture relevant seasonal events. Various CNN models have been tested, and the authors concluded with a U-Net++ with deep supervision architecture, while also applying binary focal Jaccard as loss function and Adam optimizer. It was found that in general, neural network-based approaches provided lower accuracies in comparison to water indices, while in some cases, U-Net++ outperformed the best water index-based approach (cases with unbalanced land and water peaks). It should be mentioned also that the U-Net++ approach does not involve a specific threshold, rather than being self-adjusted and assigning the probability of a pixel to be classified as water, based on the contextual information available.