Geospatial Artificial Intelligence (GeoAI): Current Status and Emerging Challenges
Alysha van Duynhoven (Simon Fraser University), Liliana Perez (University of Montreal); and Suzana Dragicevic (Simon Fraser University) will be hosting a special session sponsored by GIS Study Group on “Geospatial Artificial Intelligence (GeoAI): Current Status and Emerging Challenges” at the upcoming CAG 73rd Annual Meeting in Montreal.
Geospatial artificial intelligence (GeoAI) is a wide-ranging and expanding field evolving from the convergence of geographic information systems and artificial intelligence. Increased access to computational resources and geographic data has promoted rapid developments in theory, methods, and tools available to meet increasingly complex geographic problems that affect Canada and beyond. The aim of this session is to present the most recent developments of various GeoAI methodologies, tools, and workflows being used to meet diverse geographical challenges. We invite submissions that pertain to GeoAI, including topics such as, but not limited to: theoretical foundations of GeoAI, spatially explicit methods of Machine/Deep Learning (ML/DL) or their methodological adaptations to the characteristics of geospatial data, ethical considerations of GeoAI in research and practice, and applications of existing tools and approaches to geographic problems.
Evaluating Supervised Machine Learning Algorithms and Ensemble Models to Project Insect-Induced Forest Mortality
Roberto Molowny-Horas, Saeed Harati, and Liliana Perez
Managing Imbalanced Land Cover Change Data for Spatiotemporal Deep Learning Models
Alysha van Duynhoven and Suzana Dragicevic
Investigating the implications of environmental and ecological drivers of honeybees decline using the machine learning modeling approach
Navid Mahdizadeh Gharakhanlou, Liliana Perez, and Yenny Andrea Cuellar Roncancio
Discutant: Rob Feick and Liliana Perez