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Understanding Regional Building Characteristics in Yangon Based on Digital Building Model

Autor(en):






Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Journal of Disaster Research, , n. 1, v. 13
Seite(n): 125-137
DOI: 10.20965/jdr.2018.p0125
Abstrakt:

It is indispensable for a government to assess urban vulnerability to natural disasters such as earthquakes or flood in order to take appropriate disaster measures. However, it is sometimes difficult to obtain necessary dataset for cities or regions, especially for developing countries. The authors have been involved in a SATREPS project named “Development of a Comprehensive Disaster Resilience System and Collaboration Platform in Myanmar,” which aims to make urban vulnerability maps for Yangon City based on several datasets including building inventory of each ward. However, Yangon City has not catalogued enough data for the assessment so far. In this context, in order to understand and to arrange regional building characteristics of the city, this paper explores the possibility of using digital building model (DBM) data obtained from remote sensing imageries for the urban vulnerability assessment.

Firstly, a field survey was conducted in Sanchaung Township, and building characteristics such as structural types and the number of stories were analyzed. Therefore, DBM data was prepared for the following comparative analysis. Thirdly, additional field surveys were conducted in Latha and Pabedan Townships, located in the central business districts in the city. Finally, DBM data and the actual building data obtained by the surveys were compared to examine the applicability of DBM for building collapse risk assessment. As a result, it was found that DBM data of 3 m- 7 m represent low-rise buildings, and DBM data of more than 18 m reflect high-rise buildings.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.20965/jdr.2018.p0125.
  • Über diese
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  • Reference-ID
    10684929
  • Veröffentlicht am:
    13.08.2022
  • Geändert am:
    18.09.2022
 
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