Coastal systems are among the most vulnerable environments to climate change, facing unprecedented pressures from sea-level rise, intensifying storms, and extreme wave events. Traditional monitoring methods struggle to keep pace with the scale and complexity of these changes, while the deluge of remote sensing data (e.g., satellite imagery, LiDAR, UAV surveys) remains underutilized due to analytical bottlenecks. Artificial intelligence (AI)—encompassing machine learning (ML), deep neural networks (DNNs), and computer vision—offers transformative potential to decode these datasets, enabling high-resolution, predictive, and quantitative assessments of coastal dynamics.
This session invites contributions that harness AI to:

• ML and DNNS for automatization and enhancing coastal change detection (for past and present scenarios).

• Predict impacts of climate-driven stressors (sea-level rise, storm surges) through data-driven modeling.

• Bridge scales by integrating multi-source data (geological proxy, historical maps, real-time sensors, future projections).