Seismic vulnerability assessment of built environments with remote sensing

Kurzfassung

Global urbanization processes and increasing spatial concentration of exposed elements such as people, buildings, infrastructure, and economic values in earthquake prone regions induce seismic risk at a uniquely high level. This situation, when left unmitigated, is expected to cause unprecedented death tolls, enormous economic and ecological losses, and critical infrastructure and service failures, etc., in the future. To mitigate those perils requires detailed knowledge about seismic risks. As an important constituent element of seismic risk, the seismic vulnerability of the built environment has to be assessed. In particular, it is crucial to know about the behavior of the building inventory under a certain level of ground shaking. The main goal of the thesis was to develop and evaluate tailored methods and procedures that allow for a viable seismic vulnerability assessment of the built environment with remote sensing data. In particular, methods from the machine learning domain were adapted to estimate vulnerability levels of buildings and homogeneous urban structures based on features derived from remote sensing and by incorporation of in situ knowledge. To this purpose we deploy ensembles of earth observation sensors to exhaustively characterize the urban morphology. Empirical results, obtained for the earthquake prone cities Padang (Indonesia) and Istanbul (Turkey), confirm the viability of the approaches. Overall, this thesis provides some promising results, which show that remote sensing has a high capability to contribute to a rapid screening assessment of the seismic vulnerability of buildings and urban structures. Further work can build upon these results and may challenge empirical findings in further case studies, enhance developed and applied methods, transfer concepts and approaches to other sensor systems and data sources, or apply data and methodologies within integrative and holistic risk assessment strategies.