Effective analysis of modern geospatial big data requires more than the application of existing ML tools; it requires the development of new strategies that account for the characteristics of spatial and temporal data and processes. This session highlights advances in the methodological foundations of geospatial AI, focusing on the proposal and development of new methods, tools, and strategies to improve prediction, interpretation, and uncertainty assessment.
This session discusses challenges and advances in spatial and spatio-temporal machine learning methods and the software and infrastructures to support them. Application-based studies are also welcome, particularly those that demonstrate the practical utility of new tools or methodological approaches. By focusing on the development of methods, this session aims to bridge the gap between theoretical AI advances and practical geoscientific challenges.