Chapter

Optical Remote Sensing

Volume 3 of the series Augmented Vision and Reality pp 329-341

Date:

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

  • Diego PolliAffiliated withRemote Sensing Group, Department of Electronics, University of Pavia
  • , Fabio Dell’AcquaAffiliated withTelecommunications and Remote Sensing Section, the European Centre for Training and Research on Earthquake Engineering (EUCENTRE) Email author 

* Final gross prices may vary according to local VAT.

Get Access

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