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In the course, students acquire knowledge of how to apply remote sensing methods for environmental monitoring and modeling of human-environment systems, including the analysis of land surface changes. The course includes theory and concepts, and applies statistical and mathematical methods to analyse geodata and environmental data as well as social and socioeconomic data quantitatively. The methods include time series analysis, multivariate statistics, and machine learning, methods that are otherwise computationally demanding, especially when working with satellite images. Students are guided in conceptualising methodological frameworks that match the necessary application implemented in the lab sessions that complement the lectures. Students complete a project to demonstrate the knowledge acquired in the course. Tools and datasets from various sources, including satellite images in GEE and a cloud platform, are used.

Progressive specialisation: A1N (has only first‐cycle course/s as entry requirements)

Education level: Master's level

Selection:

Selection is usually based on your grade point average from upper secondary school or the number of credit points from previous university studies, or both.