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.
UAS analysis of fire and drought stress under controlled conditions
New update from the field work of our PhD researcher Luisa Pflumm in Kruger National Park! After weeks of fieldwork in the open savanna, Luisa's work now moves into a more controlled setting, a burn chamber experiment carried out together with our collaborators from...
