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 Python package for the development of analysis ready Sentinel-2 datacubes
We are pleased to announce that our PhD student Baturalp Arisoy has just released the open soure Python package stac2cube, dedicated to transform Sentinel-2 satellite imagery into analysis ready data. The package addresses a number of typical challenges arising during...
