Sips, MikeVassileva, MagdalenaEggert, DanielMotagh, MahdiDutta, SoumyaFeige, KathrinRink, KarstenZeckzer, Dirk2023-06-102023-06-102023978-3-03868-223-3https://doi.org/10.2312/envirvis.20231110https://diglib.eg.org:443/handle/10.2312/envirvis20231110Landslides represent one of the major threats worldwide to human life, settlements, and infrastructure. Their occurrence is increasing due to anthropogenic activities and environmental changes. Detecting slow-moving landslides in geographical space, monitoring their kinematic behavior in time, and correlating their changes in displacement to potential influencing factors (i.e., precipitation, land use change detection, and earthquakes) can contribute to forecast possible future landslide collapses. Satellite Earth Observation (EO) technology, such as Multi-temporal Synthetic Aperture Interferometry (MTI), provides millions of ground displacement time series that enable EO data scientists to detect slow-moving landslides in geographical space. In this short paper, we discuss our current Visual Analytics (VA) concept and system that supports EO data scientists to analyze ground displacement time series in a semi-automatic and exploratory manner. The goal is to derive helpful information for landslide hazard assessment, such as the location of slow-moving landslides, main kinematic parameters, changes in displacement trend, and possible correlation with external triggering factors. This paper presents the initial results of our VA system in supporting displacement classification and clustering, depicting detected clusters in the cluster overview visualization, and enabling exploratory data analysis and interactive steering.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing -> Visual analyticsHuman centered computingVisual analytics10.2312/envirvis.2023111085-917 pages