Trust, AI, and Geospatial: Building Trust in AI Enabled Geospatial Analysis

Locus Alliance - Australia's National Home for Surveying & Spatial Professionals.

Join us for our new Webinar Series.

Date: 7 July @ 1pm

Webinar Topic:

As artificial intelligence (AI) becomes increasingly embedded in geospatial analysis, we must consider how we verify the output to understand how we can trust the information generated.

AI has always been a part of the geospatial toolkit. Automated feature extraction, supervised classification to support mapping land cover, and machine learning algorithms have long been staples of the industry. What has changed recently is the scale, speed, stakes, and of course, increased application of generative AI for geospatial purposes.

Professor Matt Duckham has an extensive career history at the forefront of innovation in geospatial sciences. As the head of the Geographic Knowledge Lab at RMIT- a research group focused on combining spatial reasoning with machine learning to support better spatial decisions- he brings both the theoretical foundations and practical applications to help practitioners understand and navigate this landscape. In this webinar, Matt discusses what trust in AI enabled geospatial analysis means and how it's applied from his perspective.

Speaker:

Matt is a Professor in Geospatial Sciences and Director, Information in Society EIP (Enabling Impact Platform) at RMIT University. His research expertise covers geospatial computing, geovisualisation, and geospatial AI ("geoAI") and connects strongly with applications in areas such as emergency response, defence, transportation, and environmental monitoring.

He's an author of the textbook "GIS: A Computing Perspective", now in its third edition (GIS | A Computing Perspective | Matt Duckham, Qian (Chayn) Sun, Michae F. Worboys) and widely used in university GIS courses around the world.

Before moving to RMIT in 2015, he was a Professor in the School of Engineering at the University of Melbourne. He moved to Australia in 2004 from the US NCGIA (National Center for Geographic Information and Analysis) at the University of Maine.