Course Websites

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Geospatial Artificial Intelligence. Description: This course introduces core concepts and modern methods in geospatial artificial intelligence, focusing on spatial and spatiotemporal models based on data from remote sensing (RS), Earth observation (EO), and smart city systems. Geospatial data is inherently multi-modal?combining imagery, time series, maps, and sensor streams?and the course explores how to model such complexity using both classical and modern AI tools. We begin with foundational approaches including kriging, Gaussian processes, and spatiotemporal geostatistics. The course then moves into advanced AI-driven models, including self-supervised learning, geospatial foundation models, and architectures adapted from vision and language domains to Earth data. Applications include weather forecasting, crop type and yield prediction, resource management, and urban analytics. A hands-on component using modern geospatial ML frameworks (e.g., TorchGeo) will enable students to eng