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.
innovative urban climate in-situ measurements for Earth Observation
Bikair is a project aiming at measuring urban climate parameters with in-situ and Earth Observation. It focuses on testing low-cost Arduino-based sensors in an urban environment such as the city of Würzburg. Eventually, the project aims to correlate in-situ data with...
