Introduction to GeoAI
7.5 ECTS creditsThe course introduces fundamental principles, methods, and applications of GeoAI for the analysis and interpretation of geospatial data and for addressing environmental and other spatially related problems. The course provides an overview of data-driven approaches, including statistical analysis, machine learning, and artificial intelligence, with emphasis on their application to geospatial datasets.
Students develop practical skills in preparing, exploring, analysing, visualising, and modelling geospatial data using appropriate computational tools and programming environments. The course covers commonly used approaches for data analysis and predictive modelling, including regression, classification, clustering, dimensionality reduction, and neural networks, as well as methods for evaluating and interpreting analytical results.
Learning activities include lectures, practical exercises, computer-based assignments, and project-based work. Through an independent or group project, students apply relevant GeoAI methods to a geospatial problem, analyse and interpret the results, and communicate their findings.
Students develop practical skills in preparing, exploring, analysing, visualising, and modelling geospatial data using appropriate computational tools and programming environments. The course covers commonly used approaches for data analysis and predictive modelling, including regression, classification, clustering, dimensionality reduction, and neural networks, as well as methods for evaluating and interpreting analytical results.
Learning activities include lectures, practical exercises, computer-based assignments, and project-based work. Through an independent or group project, students apply relevant GeoAI methods to a geospatial problem, analyse and interpret the results, and communicate their findings.
Progressive specialisation:
A1N (has only first‐cycle course/s as entry requirements)
Education level:
Master's level
Admission requirements:
60 ECTS credits completed in a Science or Technology program, including 7.5 ECTS credits in Programming, and upper secondary level English 6 or English level 2. An equivalence assessment can be made.
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.