## 6 Conclusions and Future Research Directions  
Advanced remote-sensing technologies have increased significantly, integrating GIS, ML, and UAVs to develop the monitoring of coastal erosion and shoreline dynamics. Indeed, new tools offer heightened spatial and temporal resolutions, which are key elements in the effective and efficient tracking of complex shoreline change patterns. These advances [15; 21] in monitoring are centered on free-access satellite missions, Sentinel and Landsat in particular, and provide long-term data for understanding coastal dynamics for both short- and long-term time frames. The high-resolution optical data provided by Sentinel-2, together with the historical records provided by the Landsat series, form precious insights into this aspect and are a convenient source that can be used for investigating long-term changes relevant to planning for future coastal management studies [11; 39]. However, some challenges need to be considered in the future. For example, data from these different platforms must be integrated as close to real-time as possible as effective coastal monitoring requires the ability to respond to environmental changes influenced by storms and events with sea-level rise within limited time frames. Interoperability among satellites, UAVs, and ground-based sensors for multi-platform data fusion is, however, currently lacking due to the various erroneous effects, thus reducing the precision and flexibility of any monitoring system in real-world situations [44]. The integration of Synthetic Aperture Radar (SAR) data, such as Sentinel-1, still holds great potential when integrated with optical data, but especially for overcoming issues in cloud cover and adverse weather conditions. However, fully automated systems designed for real-time analysis and seamless multi-source data integration are still under construction. This should, therefore, make the research target improving machine learning models to suit a wide range of coastal scenarios. Models like WaterNet have enjoyed tremendous accuracies, but applicability is invariably narrow in scope and very often requires region-specific tuning for optimal results to be achieved [20]. These adaptations of the ML models to different coastal morphologies can extend their application globally, with a particular focus on areas with limited conventional monitoring. Lastly, it is a priority that increased access to data and the development of open-data-sharing platforms be addressed to achieve equity in management. Such initiatives could allow informed decision-making globally to support adaptation in developed and developing regions due to climate change impacts on coastal areas [3; 50]. Future research needs to focus on overcoming the fundamental problems identified above, which pose a challenge to efficient monitoring of coastal zones: the lack of standards and frameworks, inaccessibility, computational complexity of algorithms, and uneven access to advanced technology. The fact that there is no consensus on standard methods for multi-platform data integration, according to [PERSON] [36] and [PERSON] et al. [39], results in serious interoperability limitations hindering large-scale applications. The distribution of high-resolution data and computational resources themselves identified by [PERSON] et al. [49] and [PERSON] et al. [48] are uneven, which limits the scaling up of monitoring efforts in developing regions. Promising machine learning models like WaterNet show high accuracy, but the application of such models is constrained by region-specific tuning and the computational power required, as identified by [PERSON] et al. [54] and [PERSON] et al. [18]. This will also involve addressing systemic gaps in global collaboration and open-access platforms and developing adaptive algorithms that make monitoring technologies more inclusive, efficient, and responsive to dynamic environmental challenges. This is critical in building equitable solutions and enhancing resilience in communities living o coastlines worldwide.