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MobyLe - Motion by Learning

Estimating Motion of objects on Earth from Space

Credit: DLR/ESA – CC BY-SA IGO 3.0 (

Dike and infrastructure failures are events with a low-probability, but an extremely high-impact. Reliable warning systems can save lives and property. We propose an autonomous processing system based on near-continuous streams of satellite data, which enables the estimation of indicative surface motion at millimetre-precision. For a reliable estimate of these motions, additional spatial and temporal information on, for example, soil types, building age, and weather conditions should be incorporated, which enables a more reliable estimation by Artificial Intelligence (AI) techniques.

We will design, develop and demonstrate a generic toolbox for the homogenization of these additional data sources. Furthermore, an AI approach is designed and implemented which uses these homogenized datasets for the estimation of ground motion time series.

Participating organisations

Delft University of Technology
Netherlands eScience Center


Francesco Nattino
Freek van Leijen
Lead Applicant
Delft University of Technology
Marc Bruna
PhD student, advisor
Technische Universiteit Delft
Niels Jansen
Delft University of Technology
Niels  Drost
Niels Drost
Programme Manager
Netherlands eScience Center
Ou Ku
Lead RSE
Netherlands eScience Center
Ramon Hanssen
Delft University of Technology
Sonja Georgievska
Sonja Georgievska
Netherlands eScience Center

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