PICO
Modern challenges of climate change, disaster management, public health and safety, resources management, and logistics can only be effectively addressed through big data analytics. Advances in technology are generating vast amounts of geospatial data on local and global scales. The integration of artificial intelligence (AI) and machine learning (ML) has become crucial in analysing these datasets, leading to the creation of various maps and models within the various fields of geosciences. 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 spatio-temporally 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 discusses challenges and advances in spatial and spatio-temporal machine learning methods and the software and infrastructures to support them.