## 3 Results
### Results Categorization and Groupings  
Research papers have been classified into four categories based on the coastline/shoreline length where the machine learning methods have been applied, including local (<20 km), regional (20-500 km), national/transnational (>500 km), and global (Figure 1). As it can be seen, the majority of the studies were conducted at local and regional scales. Additionally, 38% of the reviewed papers have been applied in Asian countries while being followed by applications in North America and Europe (21% and 18% of the reviewed papers, respectively). Optical sensors have been widely used by the research community in the scope of coastal monitoring applications. The most widely used source of remote sensing data has been Landsat missions. Approximately half of the studies have exploited Landsat 5, 7, and 8 imageries, providing a large time series data archive. Sentinel 2 data have been also utilized, to a smaller extent (seven papers), appearing particularly during 2021 and 2022 (Figure 2). A considerable number of studies also utilized very-high-resolution data such as Google Earth composites, WorldView, RapidEye, or other aerial/satellite-based missions  
Figure 1: Categorization of reviewed papers and studies, based on their geographical scales. for extracting information from coastal features. On the other hand, very few papers have harnessed SAR data (i.e., Sentinel 1 and ALOS/PALSAR) along with machine learning algorithms for coastline extraction. Further to that, it can be seen that research studies investigate various machine learning techniques in the context of extracting shorelines/coastlines and the monitoring of relevant changes in the coastal environment. Approximately 25% of the studies assess the use of ensemble learning classifiers (Random Forest, Gradient Boosting, Extra Trees, and Adaptive Boosting), with the most frequently used algorithm to be the one of Random Forest. Additionally, quite a number of studies investigate the use of single machine learning classifiers such as Support Vector Machine, neural network-based approaches such as Multilayer Perceptron, and more complex deep learning architectures such as Convolutional Neural Networks (Figure 3). When it comes to the shoreline indicators, the majority of the studies apply different machine learning techniques for the extraction of sea-land or water/dry boundaries. In some cases, the tidal data are used as part of the classification/segmentation scheme (i.e., [81, 104]), whilst in other cases, the tidal data are used for subsequent corrections of errors in the retrieved shorelines (i.e., [92, 105, 106]). Most of the studies apply supervised machine learning techniques, and only a few cases have been identified where unsupervised methods are utilized (i.e., [74, 89, 91, 92]. Both pixel and object-based approaches have been utilized while the problem statement can be addressed either as a classification (i.e., [63, 75, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90]) or a segmentation task. The latter includes cases of thresholding segmentation that take into account mainly the spectral features of the pixels (i.e., [94]) and object-based segmentation (i.e., [85]) that in some cases is followed from classification tasks in order to yield better results (i.e., [84, 88]), and deep-learning concepts for semantic segmentation tasks (i.e., [99, 100, 101, 102]). In many cases, the extracted shorelines are merely evaluated on the basis of the applied models' metrics (i.e., [33, 76, 82, 94, 100, 107, 109]), or the validation is achieved through reference shorelines that are extracted manually from satellite images (i.e., [78, 83, 97, 99]). Only a few studies attempt to use in situ data for the validation of the models' results [81, 84, 85, 87, 89, 104], while there are cases where the authors emphasize merely on the evaluation of their proposed architecture, with the results and the extracted shorelines from other/previous methods/studies (i.e., [98, 99, 101]).