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Segment-by-segment comparison technique for generation of an earthquake-induced building damage map using satellite imagery
University of Tehran.
University of Tehran.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences. University of Tehran.ORCID iD: 0000-0003-0192-1533
University of Tehran.
2020 (English)In: International Journal of Disaster Risk Reduction, E-ISSN 2212-4209Article in journal (Refereed) In press
Abstract [en]

Known as an unpredictable natural disaster, earthquake is one of the most devastating natural disasters that causes significant life losses and damages, every year. After an earthquake, quick and accurate buildings damage identification for rescuing can reduce the number of fatalities. In this regard, Remote Sensing (RS) technology is an efficient tool for rapid monitoring of damaged buildings. This paper proposes a novel method, titled segment-by-segment comparison (SBSC), to generate buildings damage map using multi-temporal satellite images. The proposed method begins by extracting image-objects from pre- and post-earthquake images and equalizing them through segmentation intersection. After the extraction of various textural and spectral descriptors on pre- and post-event images, their differences are used as an input feature vector in a classification algorithm. Also, the Genetic Algorithm (GA) is used to find the optimum descriptors in the classification process. The accuracy of the proposed method was tested on two different datasets from different sensors. Comparing the damage maps obtained from the proposed method with the manually extracted damage map, above 92% of the buildings were correctly labelled in both datasets.

Place, publisher, year, edition, pages
2020.
Keywords [en]
Disaster management, Damage map generation, High-resolution satellite imagery
National Category
Other Engineering and Technologies not elsewhere specified
Research subject
Sustainable Urban Development
Identifiers
URN: urn:nbn:se:hig:diva-31587DOI: 10.1016/j.ijdrr.2020.101505OAI: oai:DiVA.org:hig-31587DiVA, id: diva2:1389209
Available from: 2020-01-29 Created: 2020-01-29 Last updated: 2020-01-31Bibliographically approved

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International Journal of Disaster Risk Reduction(18908 kB)8 downloads
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Jouybari, Arash

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CiteExportLink to record
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Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • sv-SE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • de-DE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
  • rtf