Fusion of Optical and SAR Data for Seismic Vulnerability Mapping of Buildings

Chapter
Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 3)

Abstract

Seismic risk depends not only on seismic hazard, but also on the vulnerability of exposed elements since it is important in providing the necessary information to policy and decision-makers in order to prevent and mitigate the loss in lives and property. Currently, the estimation of seismic vulnerability of buildings relies on accurate, complex models to be fed with large amounts of in situ data. A limited geographical scope is a natural consequence of such approach, while extensive assessment would be desirable when risk scenarios are concerned. Remote sensing might be fruitfully exploited in this case, if not for a gap between information required by current, accurate, data-hungry vulnerability models and information derivable from remotely sensed data. In this context, naturally the greatest amount of information should be collected, and data fusion is more a necessity than an option. Fusion between optical and radar data allows covering the widest range of information pieces; in this chapter we will describe how such information may be extracted and how it can be profitably fed to simplified seismic vulnerability models to assign a seismic vulnerability class to each building. Some examples of real cases will also be presented with a special focus on the test site of Messina, Italy, a notorious seismic-prone area, where an intensive campaign of data collection is in progress within our research group.

Keywords

Data fusion Very high resolution radar Building mapping Seismic vulnerability 

Notes

Acknowledgments

The authors wish to acknowledge the support of the Italian Civil Protection Department (“Programma Quadro” 2009–2011 funding of the European Centre for Training and Research in Earthquake Engineering, EUCENTRE, Pavia) and the European Commission (funding of project SAFER, 2009). They also wish to thank the colleagues at the Seismic Risk Section of EUCENTRE, particularly Helen Crowley and Barbara Borzi for their help with the SP-BELA model.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Remote Sensing Group, Department of ElectronicsUniversity of PaviaPaviaItaly
  2. 2.Telecommunications and Remote Sensing Section, the European Centre for Training and Research on Earthquake Engineering (EUCENTRE)PaviaItaly

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