## 6 Conclusions and Future Research Directions
These models together signify the vanguard of the application of AI in coastal sciences through segmentation and scene analysis to forecast the erosion of soils and model shores. It is possible that these AI advances may be integrated into existing workflows and boost the level of accuracy and the capability of resulting in better management and protection of the coastal environment. Long-term shoreline change forecasting depends on the extrapolation of historical trends using statistical and mathematical models. These include the Linear Regression Rate (LRR), Weighted LRR, and the application of Kalman Filtering in forecasting. [PERSON] et al. [2], for example, used Kalman filtering to forecast shoreline positions for 2031 and 2041 in eastern India, highlighting the importance of erosion management. Equally, [PERSON] et al., [12] modeled 2030 and 2040 shoreline positions using DSAS and trigonometric extrapolation. These models are aided by long-term Landsat archives, though their performance is limited by positional uncertainty and environmental variability. Climate data inclusion (e.g., sea-level rise projections) is increasingly becoming a requirement for reliable predictions over the longer term. Future coastal erosion monitoring developments are anticipated to arise through the fusion of new AI-powered sensors, high-resolution CubeSats, and real-time edge computing. Real-time shoreline identification and alert systems will be facilitated by AI-boosted payloads aboard UAVs or microsatellites. Hyperspectral imaging, while presently not widely offered in open access, possesses the potential to sense subtle aspects of geomorphological change and composition of sediments. Developing methods such as explainable AI (XAI) and transfer learning also permit improved interpretability and generalizability of coastal models. These technologies, combined with growing open-data missions by commercial constellations (e.g., PlanetScope), will facilitate a more comprehensive, near-real-time monitoring system for fragile coasts. This review paper emphasizes that technological innovation combined with equitable access, standardized protocols, and flexible AI models will be key in protecting vulnerable shores in the face of mounting climate stresses and human activities. Conceptualization, methodology, validation, and formal analysis: D.C. Investigation, software, resources, and data curation: D.C., E.E., N.S., C.M. and T.H. Writing--review and editing: N.S., E.E., C.M., M.E., A.C., T.H. and D.H. Visualization: D.C., C.T. and N.S. Supervision: D.H. All authors have read and agreed to the published version of the manuscript. This research was funded by AI-OBSERVER project ([[https://ai-observer.eu/](https://ai-observer.eu/)]([https://ai-observer.eu/](https://ai-observer.eu/))) titled \"Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence\", that has received funding from the European Union's Horizon Europe Framework Programme HORIZON-WIDERA-2021-ACCESS-03 (Twinning) under the Grant Agreement No. 101079468. Not applicable. The data are contained within the article and were extracted from the Web of Science(tm) database ([[https://www.webofscience.com/wos/woscc/basic-search](https://www.webofscience.com/wos/woscc/basic-search)]([https://www.webofscience.com/wos/woscc/basic-search](https://www.webofscience.com/wos/woscc/basic-search)), accessed on 2 April 2024). This research is part of the Ph.D. Thesis of D.C., hosted at the Remote Sensing and Geo-Environment Lab of the Department of Civil Engineering and Geomatics of the Cyprus University of Technology.