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.
Together with onocoy and the European Space Agency (ESA), we're starting a new project to detect and prevent GNSS fraud using AI technology and contributions from the community.
Mostafa Kiani Shahvandi has been awarded the ETH Zurich Silver Medal for his outstanding doctoral thesis titled "Geodetic Time Series Analysis and Prediction Using Machine Learning" (supervised by Prof. Benedikt Soja).
This year, six doctoral students from the Department of Civil, Environmental and Geomatic Engineering are honoured for their outstanding achievements: four of them receive ETH medals, two the Culmann Prize. Big congrats!
The Living Planet Symposium 2025 (LPS25), which takes place every three years, was held in Vienna from 23-27 June 2025. From the Space Geodesy group, Laura Crocetti and Benedikt Soja participated.
