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.
New publication on mapping mountain pine dynamics and upslope shifts in the German Alps
Mapping mountain pine remains a challenge, particularly in steep Alpine terrain where Pinus mugo forms dense krummholz thickets that are difficult to separate from surrounding vegetation and hard to survey on the ground. In our new study led by Basil Tufail, we...
