Geospatial Artificial Intelligence
The course is an elective course at second cycle level for a Degree of Master of Science (120 credits) in GIS and remote sensing and for a Degree of Master of Science (120 credits) in physical geography and ecosystem science, all specialisations.
Students will learn applications of Artificial Intelligence (AI) techniques for spatial modelling and analysis, including predictive modelling. The course has a technical focus, with special emphasize on “evolutionary optimization” and “machine learning (including deep learning)” techniques. Different applications of AI in GIS and RS will be explored in the context of exercises, seminars, and the final project. Ethical aspects of AI will also be touched in a lecture.
The course includes lectures and exercises to provide students with both theoretical knowledge and applied skills. It also includes seminars and self-learning activities, as well as quizzes to assess learning of students. There is no final exam for the course, but a final project where students have to solve a spatial problem using one of the AI techniques that they have studied in the course. All exercises are designed based on Python programming language that makes it a prerequisite for the course.
Course facts: NGEN27
Credits: 7,5 ECTS
Period: Autumn, period 2
Language of instruction: English
Prerequisites: General admission requirements and 90 credits of scientific studies are required. Of these should at least 15 credits be in basic Geographic information science, equivalent to the course NGEA11 (Geographical Information Systems basic course) 15 credits, and 15 credits in programming equivalent to NGEN20 (Programming for applications in GIS and remote sensing), 15 credits. Plus English 6/English B.