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.
AQUAMOS: New Project on Mosquito Breeding Habitats and Early-Warning Systems
In October 2026, the EORC launched AQUAMOS: “AQUAtic Breeding Habitats and Early MOSquito Warning Systems Using Remote Sensing and Environmental Monitoring”. The pilot project aims to develop a monitoring and early-warning approach for the formation of temporary water...
