MobyLe - Motion by Learning

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.