## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### Satellite Data for Coastal Monitoring
This synergy enables improved shoreline detection and change analysis across both short- and long-term timeframes. Image enhancement [46] and temporal harmonization enhanced with AI learning-powered super-resolution methods have been utilized to combine the merits of the two datasets [85]. Such methods enable more uniform shoreline extraction in areas of poor coverage due to frequent cloud coverage or seasonality, where optical imagery is inadequate on its own, such as temporal gap-filling with Landsat data, which has effectively assisted in increasing scene continuity in scenarios where Sentinel-2 imagery is partially hindered. Deep learning models, especially those that are dependent on Convolutional Neural Networks (CNNs), have better classification accuracy and maintain the spectral integrity of the fused products [20; 21; 86]. The effectiveness of this combined approach emerged through recent research. [PERSON] et al. [11] used the DSAS on stacked Sentinel-Landsat data to virtually monitor 30 years of shoreline evolution along the Da Nang coast in Vietnam. Their fusion analysis allowed for an improvement in temporal resolution, as well as the clear detection of erosion and accretion trends. In the same vein, [PERSON] and [PERSON] [41] carried out research on Sandwip Island, India, using Landsat (1988-2022) combined with Sentinel-2 imagery within the framework of DSAS-GIS. Their findings permitted the quantification of decadal shoreline changes while also estimating the mean rates of change, showing the capability of the fusion for effective long-term monitoring in dynamic delta settings. Machine learning is also at the forefront of extending the benefits of fused data. [PERSON] et al. [21] applied Sentinel-2 imagery fused with supervised classifiers for shoreline proxy delineation in several coastal settings. Their approach showed high accuracy in classification, affirming spectral fusion's use in facilitating automated shoreline extraction. Also, [PERSON] et al. [55] suggested semi-automatic shoreline extraction with the fusion of Sentinel-1 SAR with optical data for Sentinel-2. The use of the J-Net Dynamic algorithm \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Reference** & \
\begin{tabular}{c} **Geographical** \\ **and Temporal** \\ **Scope** \\ \end{tabular} & **Sentinel-1** & **Satellite Data** &
\begin{tabular}{c} **Shoreline** \\ **Change** \\ **Metrics** \\ \end{tabular} & **Key Findings** & **Unique Features** \\ \hline
[39] & Apulia Region, Italy & \begin{tabular}{c} Used for SAR \\ data in \\ shoreline extraction \\ \end{tabular} & \begin{tabular}{c} Sentinel-1 SAR, video \\ monitoring \\ systems \\ \end{tabular} & \begin{tabular}{c} Edge detection, \\ segmentation \\ \end{tabular} & \
\begin{tabular}{c} Automated \\ shoreline detection \\ method achieved \\ high accuracy, with an error of less than 3.5 pixels \\ \end{tabular} &
\begin{tabular}{c} Integrated Sentinel-1 \\ SAR with video \\ monitoring for high \\ accuracy and \\ validation \\ \end{tabular} \\ \hline
[55] & \begin{tabular}{c} Mediterranean \\ Beaches, Spain \\ (Catalunya) \\ \end{tabular} & \begin{tabular}{c} Used for \\ shoreline extraction \\ \end{tabular} & \begin{tabular}{c} Sentinel-1 SAR, \\ Sentinel-2 \\ \end{tabular} & \begin{tabular}{c} J-Net Dynamic \\ algorithm for \\ Sentinel-1 SAR, \\ shoreline extraction \\ \end{tabular} & \
\begin{tabular}{c} Achieved high \\ accuracy with \\ GNSS-RTK \\ validation, SAR \\ performed better \\ on natural beaches \\ compared to \\ man-made areas \\ \end{tabular} &
\begin{tabular}{c} Developed a robust \\ methodology (J-Net \\ Dynamic) for \\ shoreline extraction \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._considerably enhanced detection performance in the presence of artificial features, as well as varying shoreline complexity, especially where optical-only methods could not perform as expected. At broader scales, [PERSON] et al.