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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 (https://creativecommons.org/licenses/by-sa/3.0/igo/)

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
Environment & Sustainability
Environment & Sustainability

Output

Team

FvL
Freek van Leijen
Lead Applicant
Delft University of Technology
MB
Marc Bruna
PhD student, advisor
Technische Universiteit Delft
NJ
Niels Jansen
Advisor
Delft University of Technology
Ou Ku
Ou Ku
Lead RSE
Netherlands eScience Center
Niels  Drost
Niels Drost
Programme Manager
Netherlands eScience Center
RH
Ramon Hanssen
Consultant
Delft University of Technology
Sonja Georgievska
Sonja Georgievska
Meiert Grootes
Meiert Grootes
Pranav Chandramouli
Pranav Chandramouli
TvL
Thijs van Lankveld

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