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.
Monitoring Urban Heat with Multi-Sensor Earth Observation: The SMART-TWIN Project
Urban areas are increasingly exposed to heat stress, making climate-sensitive planning more important than ever. As part of this effort, our team has recently started a new project focused on urban heat monitoring using multi-sensor Earth Observation approaches. The...
