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.
EAGLE Students Explore LiDAR Data with QGIS, Whitebox, and CloudCompare
At EAGLE, our students continuously expand their expertise in Earth observation by learning various data processing techniques. Today, they are diving into LiDAR data analysis, using QGIS, Whitebox, and CloudCompare to enhance their skills in handling different remote...
