Mapping urban building stocks for vulnerability assessment - preliminary results

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.