Conveners:
Giovanni Scicchitano (Università degli Studi di Bari)
Marco Luppichini (Università degli Studi di Pisa)
Giovanni Scardino (INGV, Roma)
Artificial Intelligence (AI) is increasingly becoming a key methodological framework for the analysis, interpretation, and modelling of Earth surface processes. Advances in data availability, remote sensing technologies, and computational power have enabled the application of AI-based approaches to a wide range of geomorphological problems, from process characterization to hazard assessment and environmental change detection.
This session aims to address the potential of Artificial Intelligence as a unified framework for analysing surface processes, including coastal, fluvial, hillslope, karst, and soil-erosion systems. We welcome contributions that apply AI-driven methods to the extraction of geomorphological information from observational data, numerical outputs, and multi-temporal datasets, with particular emphasis on process-based interpretation rather than purely descriptive classification.
The session encourages studies presenting innovative and reproducible workflows, hybrid approaches integrating physical understanding with data-driven models, and applications based on remote sensing, photogrammetry, DEM-derived products, and monitoring data. Contributions discussing methodological challenges—such as model interpretability, validation, uncertainty, and scale dependency—are also particularly welcome.
By focusing on the role of Artificial Intelligence in the analysis of Earth surface processes, this session aims to fill a current thematic gap and to stimulate critical discussion on how AI can support geomorphological research in a transparent, robust, and scientifically grounded manner, with special attention to Italian and Mediterranean case studies.