Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 51-54, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B8/51/2016/
doi:10.5194/isprs-archives-XLI-B8-51-2016
 
22 Jun 2016
REFINEMENT METHOD FOR RESIDENTIAL AREA REVISION USING REMOTE SENSING IMAGE AND GIS DATA IN EARTHQUAKE RISK ASSESSMENT
A. X. Dou, X. X. Yuan, X. Q. Wang, and Z. M. Li Institute of Earthquake Science, China Earthquake Administration, Beijing, China
Keywords: Earthquake Disaster, Risk Assessment, Residential Area, Revision, Remote Sensing Abstract. This paper proposes an automatic approach for residential areas revision by means of analysing the correlation between the residential area and the topographic and geographical factors. The approach consists of four major steps: the extracting of missing residential area from the remote sensing images with high resolution; the statistic analysing on the size changes of missing residential area in each grade of the elevation, slope, distance from the road and other impact factors; modelling of residential area modification in the urban and rural region; testing the methods using 100 counties data which are located in the middle part of China North-South Seismic Belt and comparing the result to the Land Use in map scale 1:100000. The experimental results present the accuracy of urban residents by 70% increased to 89.4%, rural residents by 47% up to 81.9%, rural residents from 8% increased to 78.5%. Therefore, there is available risk exposure information in a sparsely populated area because the spatial grid distributions of population and buildings are based on the residential areas. The proposed approach in this paper will improve the accuracy of the seismic risk assessment if it is applied to the national or the whole world.
Conference paper (PDF, 1693 KB)


Citation: Dou, A. X., Yuan, X. X., Wang, X. Q., and Li, Z. M.: REFINEMENT METHOD FOR RESIDENTIAL AREA REVISION USING REMOTE SENSING IMAGE AND GIS DATA IN EARTHQUAKE RISK ASSESSMENT, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 51-54, doi:10.5194/isprs-archives-XLI-B8-51-2016, 2016.

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