Insight Moment Session on the topic of Earth Observation Techniques for Natural Hazard Risk Assessment as a Multi-Risk Methodology for Exposure Modeling.
Earthquakes do not kill people… but collapsing buildings do.
Multi-risk methodologies aid in decision-making processes that contribute to the reduction of vulnerability. They are particularly useful in modelling and quantifying the magnitude and impact of risk exposure, as well as informing decision makers from a spatial perspective. By applying earth observation techniques, decision makers have access to valuable information, enabling them to proactively adapt to risks and minimize associated damages and cascading effects.
Learn more about exposure modelling by participating in this Insight Moment by Dr. Geiss from the German Aerospace Center.
Christian Geiß (M’15) received the M.Sc. degree in applied geoinformatics from the Paris Lodron University of Salzburg, Salzburg, Austria, in 2010 and the Ph.D. degree (Dr. rer. nat.) from the Humboldt University of Berlin, Berlin, Germany, in 2014. Since 2010, he is with the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR). In 2017, he was also with the Cambridge University Centre for Risk in the Built Environment (CURBE), University of Cambridge, UK, as a visiting scholar. He is currently pursuing a habilitation project in Geography with the Julius-Maximilians University of Würzburg, Würzburg, Germany, with the title “Collective Sensing Techniques and Artificial Intelligence for Natural Hazard Risk and Impact Assessment”.
Consequently, his research interests include the development of machine learning methods for the interpretation of earth observation data, multimodal remote sensing of the built environment, exposure and vulnerability assessment in the context of natural hazards, as well as techniques for automated damage assessment after natural disasters.
Join us for this Insight Moment of the WG-RIUD on May 11th from 14:30 to 15:30h (CET time).
Recordings
Files
- Estimation of seismic building structural types using multi-sensor remote sensing and machine learning techniques5.06 MB320 downloads
- Automated building characterization for seismic risk assessment using street-level imagery and deep learning19.77 MB323 downloads
- Presentation by Dr. Christian Geiss15.49 MB171 downloads
- Large-Area Characterization of Urban Morphology—Mapping of Built-Up Height and Density Using TanDEM-X and Sentinel-2 Data12.93 MB198 downloads