## 2 Data and Methods
### Search Strategy
\begin{table}
\begin{tabular}{l c} \hline \hline
**Paper/Study Title** & **Authors** \\ \hline Machine learning and shoreline monitoring using optical satellite images: Case study of & [PERSON] et al. (2021) [84] \\ the Mostaganem shoreline, Algeria & \\ \hline Mapping mangroves extents on the Red Sea coastline in Egypt using polarimetric SAR & [PERSON] et al. (2018) [85] \\ and high resolution optical remote sensing data & \\ \hline Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression & [PERSON] et al. (2020) [86] \\ and Naive Bayes Tree Models & \\ \hline Land cover classification in Mangrove ecosystems based on VIR satellite data and & [PERSON] et al. (2020) [87] \\ machine learning-An upscaling approach & \\ \hline Hybridization of SLIC and extra tree for object based image analysis in extracting & [PERSON] et al. (2018a) [88] \\ shoreline from medium resolution Satellite images & \\ \hline Machine-Learning Functional Zonation Approach for Characterizing & [PERSON] et al. (2022) [89] \\ Terrestrial–Aquatic Interfaces: Application to Lake Erie & \\ \hline Machine Learning Approaches for Coastline Extraction from Sentinel-2 Images: & [PERSON] et al. (2022) [90] \\ K-Means and K-Nearest Neighbor Algorithms in Comparison & \\ \hline An Integrated Monitoring System for Coastal and Riparian Areas Based on Remote & [PERSON] et al. (2022) [91] \\ Sensing and Machine Learning & \\ \hline Assessment of coastal variations due to climate change using remote sensing and & \\ machine learning techniques: A case study from west coast of India & [PERSON] et al. (2022) [92] \\ \hline Automatic Coastline Extraction Using Edge Detection and Optimization Procedures & [PERSON] et al. (2018) [93] \\ \hline Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very & \\ High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine & [PERSON] et al. (2021) [94] \\ Learning Approaches & \\ \hline Change analysis on historical shorelines extracted from medium resolution satellite & [PERSON] et al. (2018b) [95] \\ images: A case study on the southern coast of Peninsula Malaysia & \\ \hline Majority voting of ensemble classifiers to improve shoreline extraction of medium & [PERSON] et al. (2017) [96] \\ resolution satellite images & \\ \hline Coast type based accuracy assessment for coastline extraction from satellite image with & [PERSON] and [PERSON] (2022) [97] \\ machine learning classifiers & \\ \hline DeepUNet: A Deep Fully Convolutional Network for Pixel-Level & [PERSON] et al. (2018) [98] \\ Sea-Land Segmentation & \\ \hline A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images & [PERSON] et al. (2019) [99] \\ \hline Sea-land Segmentation with Res-UNet and fully connected CRF & [PERSON] et al. (2019) [100] \\ \hline BS-Net: Using Joint-Learning Boundary and Segmentation Network for Coastline & [PERSON] et al. (2021) [101] \\ Extraction from Remote Sensing Images & \\ \hline SANet: A Sea-Land Segmentation Network Via Adaptive Multiscale Feature Learning & [PERSON] et al. (2021) [102] \\ \hline Application of deep learning models to detect coastlines and shorelines & [PERSON] et al. (2022) [103] \\ \hline Assessing the accuracy of Sentinel-2 instantaneous subpixel shorelines using & [PERSON] et al. (2022) [104] \\ synchronous UAV ground truth surveys & \\ \hline CoastSat: a Google Earth Engine-enabled Python toolkit to extract shorelines from & [PERSON] et al. (2019) [105] \\ publicly available satellite imagery & \\ \hline Monitoring 23 years of shoreline changes of the Zengwun Estuary in Southern Taiwan & [PERSON] (2022) [106] \\ using time-series Landsat data and edge detection techniques & \\ \hline Moving Toward L-Band NASA-ISRO SAR Mission (NISAR) Dense Time Series: & \\ Multipolarization Object-Based Classification of Wetlands Using Two Machine & [PERSON] et al. (2021) [107] \\ Learning Algorithms & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._