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Cellular automat​on (CA) are important tools that provide insight into urbanization dynamics and possible future patterns. The calibration process is the core theme of these models. This study compares the performance of two common machine‐learning classifiers, random forest (RF), and support vector machines (SVM), to calibrate CA. It focuses on the sensitivity analysis of the sample size and the number of input variables for each classifier. We applied the models to the Wallonia region (Belgium) as a case study to demonstrate the performance of each classifier. The results highlight that RF produces a land‐use pattern that simulates the observed pattern more precisely than SVM especially with a low sample size, which is important for study areas with low levels of land‐use change. Although zoning information notably enhances the accuracy of SVM‐based probability maps, zoning marginally influences the RF‐derived probability maps. In the case of the SVM, the CA model did not significantly improve due to the increased sample size. The performance of the 5,000 sample size was observed to be better than the 15,000 sample size. The RF‐driven CA had the best performance with a high sample, while zoning information was excluded.

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A new elective school subject called 'Geography-Physics' was developed by the Universities of Bonn and Bochum in cooperation with a German high school. With a focus on remote sensing, the modules of this STEM subject convey information, and present methodology and applications. There are two main sections: the physics of remote sensing, including both mathematics and computer science, and the geographic applications. GIS is a major part of the exploitation of Earth Observation data, but the use of GIS and EO data is not feasible in school lessons due to financial and time constraints. Instead, small specialized GIS tools with embedded EO imagery are used. The tools were developed by two projects, FIS and Columbus Eye/KEPLER ISS, and evaluation and meetings with experts were conducted in close cooperation with the partner school. The first 2-year course of the new subject was completed in summer 2018. The teachers implementing the course have since re-evaluated their concept and revised the syllabus to enhance applicability in professional contexts, to reduce redundancies with other subjects, and to ensure that the overall content fits into the allotted number of teaching hours. The pupils also evaluated the materials and the subject.