MultiSat4Slows System for Detecting and Assessing Potentially Active Landslide Regions -- Initial Results from an Ongoing Interdisciplinary Collaboration

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dc.contributor.authorSips, Mikeen_US
dc.contributor.authorVassileva, Magdalenaen_US
dc.contributor.authorEggert, Danielen_US
dc.contributor.authorMotagh, Mahdien_US
dc.contributor.editorDutta, Soumyaen_US
dc.contributor.editorFeige, Kathrinen_US
dc.contributor.editorRink, Karstenen_US
dc.contributor.editorZeckzer, Dirken_US
dc.date.accessioned2023-06-10T06:06:24Z
dc.date.available2023-06-10T06:06:24Z
dc.date.issued2023
dc.description.abstractLandslides 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.en_US
dc.description.sectionheadersClimate, Land use, and Biodiversity
dc.description.seriesinformationWorkshop on Visualisation in Environmental Sciences (EnvirVis)
dc.identifier.doi10.2312/envirvis.20231110
dc.identifier.isbn978-3-03868-223-3
dc.identifier.pages85-91
dc.identifier.pages7 pages
dc.identifier.urihttps://doi.org/10.2312/envirvis.20231110
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/envirvis20231110
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing -> Visual analytics
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.titleMultiSat4Slows System for Detecting and Assessing Potentially Active Landslide Regions -- Initial Results from an Ongoing Interdisciplinary Collaborationen_US
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