Mapping urban building stocks for vulnerability assessment – preliminary results

Abstract

This paper discusses a methodology to collect building inventory data by combining image processing techniques, field work or tools such as Google Street View and applying statistical inferences. Following the methodology outlined in Marinescu (2002), a family of Gabor filters are first constructed, which are then applied to an optical high-resolution image. The output from the processed image is segmented using Self-Organising Maps. This paper examines the relationship between the segmented areas in the image and the building type distribution within each segmented area, by deriving the distribution from field data. The relationship between the average number of buildings in these cells against the number of grid cells allocated to each segmentation cluster is also investigated. Finally, using these results, the overall building inventory distribution for the whole of the case study site of Pylos is presented.

Keywords:

• building inventory data collection
• remote sensing
• high-resolution optical satellite images
• Gabor filters
• Self-Organising Maps
• field data

Acknowledgements

The authors would like to thank Willis Research Network for providing the funding to carry out this study and their continuing support. We would also like to thank Antonios Pomonis and Maria Gaspari from the SEAHELLARC project, who kindly provided access to the Quickbird satellite image of Pylos used in this study as well as access to ground validation data collected through their field surveys for SEAHELLARC.

Notes

1. The Quickbird image of Pylos was obtained courtesy of the EU funded (FP6) project, SEAHELLARC.

2. SEismic and tsunami risk assessment and mitigation scenarios in the western HELLenic ARC. Available from: http://www.seahellarc.gr/[Accessed 8 July 2010].