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.
Successful MSc Thesis Defense by Sharmin Mim on Ability of Random Forest Model to Predict the Mean and,Standard Deviation of Winter Wheats Biomass in Bavaria: A Case Study
Congratulations to Sharmin Mim on the successful defense of her MSc thesis, titled "Ability of Random Forest Model to Predict the Mean and Standard Deviation of Winter Wheat Biomass in Bavaria: A Case Study." Her work focused on the Rhön region, where she combined...
