Advances in data availability, computational methods, and modeling frameworks are transforming our ability to understand Earth system processes and their interactions with human and built environments. Geospatial artificial intelligence (GeoAI) and related approaches enable integration of observations, models, and domain knowledge across spatial and temporal scales, while emerging paradigms such as Earth foundation models support synthesis of large-scale, multi-modal data for improved representation and prediction.

This session invites contributions that develop or apply GeoAI and integrative modeling to study environmental change, natural hazards, and infrastructure systems. We welcome methodological and applied studies combining remote sensing, in situ observations, and process-based or hybrid models. Topics include multi-scale Earth–human system interactions, monitoring and modeling of hazards, and assessment of infrastructure risk and resilience, as well as emerging approaches such as digital twins and large-scale data integration frameworks supporting analysis and decision-making.

• A - Atmospheric Sciences
• IN - Informatics
• NH - Natural Hazards
• SY - Science and Society