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CSC 716 Geospatial Artificial Intelligence
This course delves into recent breakthroughs in AI and deep learning, specifically their application to analyzing large-scale geospatial and spatiotemporal data. We will emphasize both the theoretical foundations and practical applications. Students will gain a comprehensive understanding of the concepts and principles behind GeoAI. In particular, basic characteristics of big spatial and spatiotemporal data, spatial relationships, types of learning (statistical, semisupervised, transfer, active, federated), semantic segmentation, change detection, geospatial object-based image analysis, geospatial knowledge-guided ML, geospatial representation learning, geospatial foundation models (fine-tuning, prompting, and Retrieval-augmented generation), visual question answering, geo-simulations and digital twins, location-based services and recommender systems, Geographic bias and fairness, and privacy and explainability. The course will equip students to not only apply these techniques but also evaluate and compare various models using benchmark datasets. Working knowledge of algebra, calculus, and Python and basic understanding of ML and deep learning recommended.
Typically offered in Fall only