PICO
Modern challenges in climate risk management, disaster response, public health, resource management, and logistics demand robust spatiotemporal analysis of increasingly complex geospatial datasets. Recent studies, however, highlight significant challenges when applying ML and AI to spatial and spatio-temporal data along the entire modelling pipeline, including reliable accuracy assessment, model interpretation, transferability, and uncertainty assessment. This gap has been recognised and led to the development of new spatiotemporally aware strategies and methods in response to the promise of improving spatio-temporal predictions, the treatment of the cascade of uncertainties, decision making and facilitating communication.
This session focuses on the strategic integration and application of artificial intelligence (AI) and machine learning (ML) to address these challenges. We welcome contributions that explore novel methods, software tools, and infrastructures designed to improve spatiotemporal predictions, manage cascading uncertainties, and support decision-making. Emphasis will be placed on interpretability, transferability, and reliability across the modelling pipeline, as well as on the communication of results to diverse stakeholders. Case studies, theoretical advances, and cross-disciplinary approaches are encouraged.