## 5 Discussion
### Scientific Gaps and Limitations in Model Generalization  
Identifying and emphasizing scientific gaps indicates that real-time, multi-platform integrated data are crucial, especially as significant advancements have been made in individual remote sensing platforms, such as satellites, UAVs, and ground-based sensors. However, the generally low interoperability between these platforms remains a critical bottleneck for effective coastal monitoring, particularly in addressing rapid environmental changes caused by storms or erosion events [42; 67]. The number of data fusion techniques has grown, increasingly merging Sentinel-1 SAR with Sentinel-2 optical data to enhance monitoring, but these are constrained by weather conditions [46]. Most current data fusion techniques seldom incorporate high-resolution UAV data, which could complement satellite data by addressing temporal and spatial resolution gaps [8; 19]. Furthermore, machine learning models, such as WaterNet, show promise but perform well only in localized regions; they lack adaptability across diverse coastal landscapes, primarily due to the need for region-specific tuning, limiting their broader applicability [7; 45]. Another key focus is addressing the pressing need for enhanced access to open data, as well as the data gap between regions, with specific attention to developing countries, which are usually at the front lines of most climate-related impacts with less advanced monitoring. Machine learning models must be adapted to work in data-poor situations to achieve equity globally in coastal monitoring. Also, creating open-access platforms for data sharing and predictive models could foster further collaboration and perhaps allow coordinated responses to coastal challenges worldwide.  
Despite these achievements, predictive modeling for climate-induced shoreline shifts is still in its infancy, with many models lacking robustness in incorporating long-term sea-level rise and extreme weather patterns [19]. These scientific gaps highlight the urgent need for more cohesive, scalable systems that effectively integrate data sources and adapt to diverse coastal settings.  
Regarding enhancement in coastal monitoring, the challenges related to scalability and data integration remain. Satellite remote sensing, in conjunction with recent technologies involving machine learning, has greatly enhanced the capability for shoreline change monitoring, but the integration of datasets across platforms is a key challenge. Several studies have highlighted the potential of dataset combinations from sources like Sentinel-1 SAR, Sentinel-2 optical data, UAV imagery, and ground-based sensors. For example, [PERSON] et al., 2022 [44] combined drone and satellite data to enable high-resolution temporal monitoring of coastal dynamics, showcasing the added value of such multiplatform approaches. However, this often remains compromised due to interoperability or standardization aspects between the platforms, thereby complicating seamless integration into most such efforts. Furthermore, methodologies aiming at data fusion are limited by computational demands and the absence of structured globally accepted frameworks. Studies like those by [PERSON] et al., 2023 [55] and [PERSON] et al., 2022 [21] highlight that while combining SAR and optical data offers enhanced insights, it requires significant preprocessing, specialized algorithms, and computational resources that are not universally accessible. Additionally, the use of UAV data is still largely unexamined, even though it can potentially address gaps in temporal and spatial resolution. Studies like [PERSON] et al., 2022 [58] emphasize that combining UAV imagery with satellite data has the potential to yield a more complete understanding of coastal dynamics; however, these integrations are infrequently seen due to the absence of standardized workflows.