NDLI: A support vector regression approach for building seismic vulnerability assessment and evaluation from remote sensing and in-situ data

Content Provider	IEEE Xplore Digital Library
Author	Panagiota, M. Jocelyn, C. Erwan, P. Philippe, G.
Copyright Year	2012
Description	Author affiliation: Grenoble Images Parole Signal Automatique (GIPSA-lab), France (Panagiota, M.; Jocelyn, C.) \|\| ISTerre, University Joseph Fourier-Grenoble, France (Erwan, P.; Philippe, G.)
Abstract	In this paper, seismic vulnerability assessment is addressed under the umbrella of remote sensing. A study for estimating and evaluating information for assessing seismic vulnerability based on a building basis is presented. The proposed methodology utilizes the capabilities of remote sensing and combines in-situ data tested in the area of Grenoble (France). A map is estimated in agreement with in-situ data, as support information system for seismic risk in the context of building vulnerability assessment. In the methodology proposed, building attributes such as roof identification, building height and characteristic scale are extracted from very high resolution panchromatic data, and an accurate digital elevation model. Support vector machine regression is used to estimate building vulnerability and in-situ data are available for evaluation.
Starting Page	7533
Ending Page	7536
File Size	600965
Page Count	4
File Format	PDF
ISBN	9781467311601
ISSN	21536996
e-ISBN	9781467311595
e-ISBN	9781467311588
DOI	10.1109/IGARSS.2012.6351888
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-22
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Buildings Remote sensing Feature extraction Support vector machines Training Kernel Correlation in-situ data Seismic Vulnerability Urban Area Support Vector Machine Regression remote sensing data
Content Type	Text
Resource Type	Article

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