DocumentCode :
3445493
Title :
Classification of building structures on high spatial resolution satellite image for mapping the readiness to hurricane hazards, Cancun and Chetumal, Mexico
Author :
Tao Tang ; Montes, Elias ; Omar, Murad ; Snyder, Eric ; Boci, Aaron
Author_Institution :
Dept. of Geogr. & Planning, State Univ. of New York - Buffalo State, Buffalo, NY, USA
fYear :
2013
fDate :
20-22 June 2013
Firstpage :
1
Lastpage :
8
Abstract :
This research analyzes the spatial clusters of low quality building structures in Cancun and Chetumal, Mexico applying high spatial resolution satellite images. The objective of this research is to visualize the vulnerable areas of building structures in the two cities, in hopes of helping for the planning the disaster management and mitigating damages during a hurricane event. Five categories of classification schemes were established for five major building structures quality criteria. Combining field sampling and maximum likelihood image pixel clustering testing, the results show that property and human life loss during a hurricane event are likely strongly associated with building structure quality and design. Low quality of building materials with poor design, in particular the self-built houses of low income families encounter much higher risk.
Keywords :
artificial satellites; building materials; buildings (structures); classification; design engineering; disasters; image recognition; pattern clustering; storms; Cancun; Chetumal; Mexico; building materials; building structure classification; disaster management; high spatial resolution satellite image; hurricane hazards; mapping; planning; poor design; spatial clusters; Buildings; Cities and towns; Hazards; Hurricanes; Planning; Satellites; Spatial resolution; building structure quality; hazard mapping; high spatial resolution satellite image; spatial decision support (SDS);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoinformatics (GEOINFORMATICS), 2013 21st International Conference on
Conference_Location :
Kaifeng
ISSN :
2161-024X
Type :
conf
DOI :
10.1109/Geoinformatics.2013.6626086
Filename :
6626086
Link To Document :
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