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.
Building Capacity for Climate Research: Remote Sensing Training with West African PhD Students
For two weeks, our NetCDA Team, this time formed by our colleagues Lilly Schell and Michael Thiel, is back at the Kwame Nkrumah University of Science and Technology (KNUST) as part of the WASCAL Graduate School on Climate Change and Land Use, supporting PhD candidates in developing their own skills on remote sensing analyses related […]
