Artificial Intelligence (AI) is rapidly transforming geomorphological research, offering powerful tools for the analysis, modeling, and prediction of Earth surface processes and interplanetary studies. In recent years, the availability of high-resolution environmental data, open-source software, and cloud-based infrastructures has facilitated the widespread adoption of machine learning and deep learning techniques across the geomorphological community. Applications now span multiple domains, including coastal dynamics, fluvial morphology, hillslope processes, karst systems, and soil erosion, with tasks ranging from landform classification to hazard mapping and change detection.
This session welcomes contributions exploring the integration of AI and data-driven methodologies in geomorphology. We invite studies applying supervised and unsupervised learning, neural networks, ensemble models, object detection, clustering, or dimensionality reduction techniques to analyze geomorphic features and processes. We particularly encourage submissions focusing on innovative workflows, reproducible pipelines, transfer learning, hybrid modeling, and the integration of remote sensing or DEM-derived datasets. Contributions addressing methodological challenges, model interpretability, and equity in data and tool access across different geographic regions are also welcome. Furthermore, contributions on remote sensing studies and AI applications in interplanetary geomorphology represent a step forward for our session.
The session aims to foster interdisciplinary dialogue among geomorphologists, data scientists, and Earth system modelers, and to outline future research directions toward a more transparent, inclusive, and methodologically robust use of AI in the geomorphological sciences.