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.
New EO4CAM Video: Supporting Climate Adaptation with Earth Observation Data
Climate adaptation requires reliable information, long-term monitoring, and evidence-based decision-making. Our EO4CAM short video highlights how Earth Observation (EO) and satellite-based data can support public authorities and decision-makers in identifying and...
