An introductory course to spatial artificial intelligence (AI), providing a big picture of spatial AI applications (e.g., Google Maps, Uber/Lyft, Earth observation, smart cities, autonomous vehicles), techniques, platforms, trends, debates, etc. The course will cover basics of AI, identify challenges faced by AI techniques in the context of spatial data and applications, and introduce spatial-aware AI methods to address them. AI topics include but are not limited to: spatial data models and knowledge representation, pattern mining, machine learning, perception, planning, etc. Students are expected to have a broad understanding of spatial A concepts, develop intuitions and insights to AI techniques, and have some hands-on experience (Python) at the end of the course.

Credits: 3
Grading Method: Regular, Pass-Fail, Audit