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.
About The Author
Akam. Rat (sen. lecturer, Ass. Prof.) for Remote Sensing in Biodiversity and Conservation. Teaching within the Global Change Ecology study program: Remote Sensing application in Ecology. Mainly using OpenSource software: R, GRASS, QGIS, Latex, beamer, knitr, tikz/pgf
New publication: LiDAR-based simulation of tree-and stand development after bark beetle disturbances
January 3, 2017
July 15, 2014
September 10, 2019