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Risk assessment, irrespective of the natural hazard or hazards of concern, heavily relies upon reliable knowledge about the characteristics of the built-up environment that best relate to its vulnerability. As exposure data collection represents a resource-intensive process, in most large-scale applications, desk studies are carried out. Decreasing the scale of the application, for instance for city-scale or scenario-based impact assessment, more direct information on the building stock is required. This becomes more complicated when multiple hazards need to be considered, which is in fact a fairly common situation.
The increasing quality and decreasing cost of mobile mapping technologies, combined with the flexibility of custom-made, web-based collaborative platforms, allow for a new paradigm in data collection and integration to be developed. On the one hand, more efficient spatial sampling approaches can be applied, leading to the better use of available resources and more actionable information to be collected, while also exploiting ancillary data from alternative technologies, such as remote sensing. On the other hand, a more transparent workflow may be established, that aims at minimizing the subjectivity in the data collection process.
The availability of traceable, reliable “atomic” observations furthermore allows a consistent spatial modelling of exposure and vulnerability information, actively contributing to a better characterisation of the underlying uncertainties. This also provides the necessary feedback for implementing dynamic, incremental models.
Several activities carried out over recent years provide preliminary evidence that support such a paradigm. Operational applications in the field of risk assessment and post-earthquake reconnaissance will be shown and discussed.
GFZ Potsdam GeoForschungsZentrum Potsdam, Germany
Massimiliano Pittore is a senior scientist at GFZ Potsdam. He holds a Masters of Physics and a PhD in Informatics and Systems Engineering from the University of Genova, Italy. He joined GFZ in 2010 after a significant experience as a researcher and R&D manager in the high-tech-related private sector. His primary expertise is in seismic vulnerability and risk assessment, remote sensing, image processing, pattern recognition and statistical learning.
Since 2011 He has been working on earthquake early warning and rapid response, and coordinates the development of innovative methodologies for the rapid and efficient collection of seismic exposure data for risk assessment, combining remote sensing, mobile mapping, machine learning and geo-statistical modeling. He has undertaken activities (surveys, training, assessment) in Europe, Central Asia, Turkey and South and Central America.
Since 2017 he leads the group “Early Warning and Impact forecasting” in the section Seismic Hazard and Risk Dynamics, at GFZ.