Dean's Doctoral Initiative: Geospatial Technologies

Geospatial Information Technology bannerThe Faculty of Environment announces 35 funded doctoral opportunities ($100,000 over four years each) for domestic students 

Browse the table below to find the researcher and project that suits you. If you find something interesting, click the researcher's name to contact them directly and learn more!

To apply please connect with Simron Singh, Associate Dean, Graduate Studies (; 519-888-4567, ext. 33111).

Researcher Project
Rob Feick Geospatial methods for supporting public participation in planning
Rob Feick Methods for detecting place-based conflict in volunteered geographic informationData quality tools for citizen science water observations 
Rob Feick Mobile spatial decision support for citizen science and community planning
Rob Feick Data quality tools for citizen science water observations 
Chris Fletcher Using numerical climate models to investigate large-scale climate dynamics, variability and change
Peter Johnson

Geospatial open data, open government, and the use of artificial intelligence for governance

Suzanne Kearns                 Aviation safety and human factors, aviation training (including competency-based education and training technologies), or training of remotely-piloted aircraft system ("drone") operators
Richard Kelly Radar remote sensing of snow using ground-based radar instruments
Richard Kelly Satellite radar observations of snow using InSAR techniques
Richard Kelly

Combining multi-sensor satellite remote sensing observations of snow

Jane Law Analyzing changes in urban or health planning: a Bayesian spatio-temporal analysis approach
Jane Law Study of individual and place/neighbourhood effects in urban planning: a multilevel Bayesian spatial analysis approach
Jane Law Applications of advanced spatial analysis in public health, housing, immigration, crime, transportation, or demography studies
Jane Law Associations between healthy communities (or specific health outcomes) and the built environment: a Bayesian spatial analysis approach
Jonathan Li

and co-advisor Prof. Dr. Michael Chapman (GEM Adjunct Professor)

Land Use and Land Cover Change Detection Using Deep Learning Techniques

Jonathan Li

and co-advisor Prof. Dr. Michael Chapman (GEM Adjunct Professor)

Autonomous Driving Using 3D High-Definition Maps Generated by Laser Scanning Point Clouds