## 3 Results  
### Literature Analysis and Main Findings  
[PERSON] and [PERSON] (2021) [74] investigated the socio-economic impact of cyclones in coastal areas through the use of Sentinel 2 and Landsat imagery. Remote sensing indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) were used for changes analysis in order to monitor water bodies patterns, whilst a supervised classifier (Maximum Likelihood) was used for land-use classification. The classified imagery was used for monitoring change detection dynamics. Moreover, an unsupervised classification method was also evaluated (ISODATA), whilst desktop software (i.e., ArcGIS, ENVI, ERDAS, etc.) were utilized for pre-processing and spatial analysis tasks. The authors concluded that the coarse resolution of satellite data hindered the more precise change detection and micro-level analysis. [PERSON] et al. (2022) [75] employed supervised classification models (Logistic Regression, Random Forest, and eXtreme Gradient Boosting), aiming to assess the impacts of 15 selected human and environmental variables on the coastal land loss probability at different time instances. The approach was initiated with an analysis of the spatial and temporal patterns affecting land loss in the area of interest, including the quantification of the model predictors at given time instances, as well as the short- and long-term forecasts. It was seen that the performance of non-linear ensemble models was better than the one of Logistic Regression expressing a linear relationship between coastal loss probability and the variables. However, the approach does not take into consideration climate change related variables, whilst it is acknowledged that the use of additional and of higher resolution satellite data could lead to more reliable/accurate results. A model relying on EO data towards the estimation of accretion rates in coastal wetlands was introduced by [PERSON] et al. (2022) [76]. Time series of Landsat data, along with available accretion records for the areas of interest, were employed towards the production of proxy variables that contribute to the phenomenon. These include mean NDVI and total suspended solids estimates within water pixels. A Random Forest regression took place, showcasing a good relationship between the above-mentioned utilized variables, whilst the inclusion of elevation and distance factors further improved the estimations of sediment deposition. [PERSON] et al. (2015) [77] employed Support Vector Machines for the identification of the spatial patterns of coastline changes. Initially, a binary classification scheme was utilized for the discrimination of land and water classes. Then, a training dataset was shaped from Landsat images through a random sampling strategy, and it was used for the development of a multi-class Support Vector Machine pair-wise classification. The method presented a difference in the order of 5-20%, in comparison to photo-interpretation-based approaches. The difference can be attributed to the fact that the Support Vector Machine approach is not applied on a sub-pixel level, and thus the accuracy of the classifiers is reduced, especially when images with coarse spatial resolution are used. In areas with a larger degree of heterogeneity and diversity, the issue can be even more significant. A similar approach was applied by [PERSON] and [PERSON] (2022) [78], using Support Vector Machines on optical coastal imagery to determine the magnitude of coastal erosion, whilst the ArcGIS suite was also applied for the spatial analysis of the extracted shorelines. The ability of the SVM algorithm to efficiently estimate the coastal erosion process was showcased in the study of [PERSON] et al. (2020) [79], where the algorithm was compared with other supervised classification methods towards assigning an image into three classes (foam, water, and sand). [PERSON] et al.