Urbanization has dramatically transformed global land use and this trend is expected to continue. Knowing the processes of urban land transition is fundamental to interpreting global environmental change, yet they remain insufficiently characterized due to their complexity and heterogeneity. Recent advances in geospatial data availability, AI, and computational capacity provide unprecedented opportunities to better represent these processes. This session invites contributions that leverage GeoAI to advance our understanding of urban land transition processes across scales. Topics of interest include, but are not limited to, uncertainty quantification of existing models and data products, development of novel GeoAI approaches, characterization of spatiotemporal patterns, mapping and modeling of land transition stages, and integration of land transition processes in applications related to urbanization modeling, climate resilience, food security, and human health. Submissions addressing key methodological and practical challenges such as model generalization, time-series analysis, weakly supervised learning, and land competitions are particularly encouraged.

• B - Biogeosciences
• IN - Informatics