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.
Poster Presentation at AK Hydrologie, Bonn
From November 13 to 15, Sofia Haag and Christian Schäfer attended the AK Hydrologie workshop in Bonn, where they presented their work from the EO4CAM project. The first day featured an insightful field excursion to the Ahrtal region, led by Prof. Dr. Jürgen Herget,...
