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. 

Some of these places are not like the others:  Using machine-generated methods to explore place variability within and across city zones.

Rob Feick and Colin Robertson

Geomatics for sustainable energy: towards a spatial model of industrial waste heat recovery for heating residential and commercial buildings in Montreal

Saeed Harati, and Liliana Perez

Using Artificial Intelligence Wisely: Age-Friendliness, ‘Snow Moles’ and Deep Mapping Urban Perception

Zhewen, L., Sawada, M., Kristjansson, E., Baxter, D, and Dignam M.

Flood susceptibility evaluation through ensemble machine learning models

Navid Mahdizadeh Gharakhanlou, and Liliana Perez