The Chair of Space Geodesy faces the challenges and profits from the opportunities that come with “big data” in geodesy. We design and implement advanced machine learning techniques to automate the processing of GNSS and VLBI data, resulting in a faster availability and higher quality of geodetic products. Also, machine learning is applied in the large-scale analysis of GNSS time series to detect and investigate geophysical signals, inter alia related to hydrological and seismic events, as well as to severe weather events and ionospheric disturbances. Finally, we improve the predictions of several types of geodetic parameters by machine learning. In particular, we focus on determining future Earth orientation parameters, station coordinates and atmospheric parameters.
Last week, Matthias had the opportunity to attend a workshop on "Science and Technology with the Wetterstein Millimeter Telescope (WMT)" at the Umweltforschungsstation Schneefernerhaus, located 2650 meters above sea level on the Zugspitze (Germany).
On the 12th of June, Yuanxin Pan successfully defended his Ph.D. thesis entitled "Characterization of Reflected and Low-cost GNSS Signals with Machine Learning".
On the 7th of May, Lukas Müller successfully defended his Ph.D. thesis entitled "Orbit Determination for LEO Constellations Based on Single-Satellite and Network Processing of GNSS Data".
