## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### Applications of Machine Learning and Artificial Intelligence in Coastal Monitoring  
Recent developments in Machine learning (ML) and Deep Learning (DL) techniques have further reiterated the role of ML and DL in the monitoring and analysis of different environments, especially coastal ones, for shoreline change detection and management. Integrating different Artificial Intelligence (AI) techniques enables the proper mapping \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **UAV-Based Monitoring** & **Satellite-Based Monitoring** & **References** \\ \hline \multirow{4}{*}{Spatial Resolution} & Ultra-high (cm-level) resolution imagery and terrain models. Can detect fine features like beach cusps, berms, dune lines. & Moderate to high (meter-level) resolution (e.g., 10 m Sentinel-2, 30 m Landsat). Small features are often generalized. & [44,88,89] \\ \hline \multirow{4}{*}{Positional Accuracy} & Centimeter to decimeter accuracy with RTK/PPK GNSS or GCPs. Enables sub-meter shoreline mapping. & Typically \(\sim\)5–10 m horizontal uncertainty. Subpixel methods improve precision, but less accurate than UAVs. & [54,88,89] \\ \hline \multirow{4}{*}{Revisit Frequency} & On-demand deployment. Enables site-specific, timely mapping before/after events (e.g., typhoons). Limited by weather/logistics. & Scheduled orbits: Sentinel-2 (5 days), Landsat (16 days). Enables consistent long-term data but may miss rapid changes or specific tidal events. & [89, 90, 91] \\ \hline \multirow{4}{*}{Coverage Scale} & Best for local to sub-regional sites (a few km\({}^{2}\) per flight). Requires multiple missions for broader areas. & Large-scale coverage (100 s of km\({}^{2}\) per image). Suitable for regional or global comparisons. & [44,89,90] \\ \hline \multirow{4}{*}{Cost} & Equipment, personnel, and post-processing make it costly at scale. Efficient for small area studies. & Low per-area cost due to free sources like Sentinel and Landsat. Commercial data also cost-effective at large scale. & [88,90] \\ \hline \multirow{4}{*}{Integration Potential} & Ideal for in-situ calibration and validation (e.g., DEMs, GCPs). High flexibility complements satellite gaps. & Sets regional context. Satellite data guides UAV deployment. Enables fused multi-scale shoreline monitoring frameworks. & [6,31,38,44] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative evaluation of UAV-based and satellite-based shoreline monitoring methods across key technical and operational parameters. The table summarizes differences in spatial resolution, positional accuracy, revisit frequency, and integration potential, supported by peer-reviewed studies published between 2021 and 2024 as our research indicated. of shorelines efficiently and precisely to allow erosion prediction and long-term coastal monitoring, with less human intervention and increased accuracy [20]. Table 8 (application of AI methods for automated shoreline change detection and coastal monitoring) illustrates the research on AI methods applied to automated shoreline change detection and coastal monitoring. Some of the outstanding studies in our review criteria are highlighted, in which AI-based models have been applied to improve shoreline monitoring activities using different methods. [PERSON] et al. [20] developed a new model technique, WaterNet, which is an ensemble deep learning model designed to automatically extract shorelines from satellite images. The Intersection over Union (IoU) and F1 scores are crucial metrics for evaluating the accuracy of segmentation models, particularly in shoreline extraction, where they measure the model's ability to differentiate between land and water in satellite images. The IoU assesses the overlap between the predicted and actual shorelines, with a high score of 99.59% indicating exceptional model accuracy. The F1 score, a harmonic mean of precision and recall, balances these two metrics, with a remarkable score of 99.79% highlighting the model's precision and comprehensiveness in identifying shorelines.