Geospatial Artificial Intelligence

The overarching aim of the course is to introduce the student to new paradigms in data management with special focus on artificial intelligence (AI) and machine learning (ML) and their application in GIS and remote sensing.

Course content

The course starts with a general introduction to the concept AI and its different components with a focus on GIS-applications. This is followed by modules with a focus on optimisation of data processing, machine learning and simulation techniques for applications in both GIS and remote sensing. Main focus for the course is technical knowledge and technical proficiencies aiming to that the student should be able to apply AI in different situations, but aspects of ethics and public benefits are also treated in lectures during the course.

Competence and skills on completion

On completion of the course, the students shall be able to:

• independently use AI for so-called "spatial data mining and knowledge "discovery", and thereby process large amounts of spatial data and explore and develop knowledge
• apply AI in spatial simulation and modelling
• apply AI and ML for classification of remote sensing data in the form of satellite images in relevant application fields as e.g. land use mapping

Course design

The teaching consists of lectures, practical exercises, seminars and a final project assignment that is carried out individually or in groups. Each theme is highlighted with practical exercises that, based on key elements, expands and deepens the understanding of the theoretical material. Through the exercises, the student gets ability to apply AI on different spatial problems to develop solutions.
Both exercises and seminars aim also to deepen the students' commitment in their own learning process. Participation in exercises, seminars, laboratory sessions and project work, as well as associated parts, is compulsory.