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.
🌍 Earth Observation News: Wrapping Up 2025, Looking Ahead to 2026
As 2025 draws to a close and 2026 begins, our Earth Observation Research Cluser News Blog on Remote-Sensing.org offers a rich overview of a year defined by scientific diversity, collaboration, and real-world impact. From peer-reviewed publications and innovative...
