Jakob Schwalb-Willmann just started his M.Sc. thesis titled “A deep learning movement prediction model using environmental data to identify movement anomalies”. He will combine animal movement and remote sensing data in order to develop a generic, data-driven DL-based model that predicts movements from movement history alongside environmental covariates in order to detect movement anomalies. He will establish simulated, controlled environments that allow precise adjustments of the model inputs to test the model’s feedbacks and its variability. It can be considered as a precursor study for the model’s deployment on real data and to only experimentally apply it on such due to the given constraints (time and content) of his M.Sc. thesis.
Earth Observation Meets Agricultural Practice: The Final Excursion of the EAGLE Block Course
The EAGLE block course "Linking Science and Practice in Earth Observation for Climate Adaptation" concluded with a visit to the Bavarian State Office for Agriculture (LfL) and its Research Center for Agriculture in Dry Regions in Schwarzenau. Following earlier visits...
