@article{ref1,
title="Classifying building occupancy using building laws and geospatial information: a case study in Bangkok",
journal="International journal of disaster risk reduction",
year="2017",
author="Arunplod, Chomchanok and Nagai, Masahiko and Honda, Kiyoshi and Warnitchai, Pennung",
volume="24",
number="",
pages="419-427",
abstract="Classifying building occupancy types helps build inventory databases, which are important in disaster management. These databases are used to estimate disaster loss and vulnerable to disaster. Oftentimes, this information is gathered via remote sensing data, which can help determine the shape of buildings. However, in order to be helpful, such records require precise occupancy information to support pre-disaster preparation and post-disaster recovery planning. Collecting this information is time-consuming and costly, especially in developing countries with limited resources, such as Thailand. This study offers a potential solution to these hurdles, using a combination of building construction laws and regulations on building occupancy and geospatial data to develop a new methodology for developing a database of building occupancy classification data. The results reveal that this method is effective in classifying urban building occupancy, with more than 75% overall accuracy. Therefore, these methods can be useful in the disaster management of national or local governments in countries where the means to collect such data is limited.<p /> <p>Language: en</p>",
language="en",
issn="2212-4209",
doi="10.1016/j.ijdrr.2017.07.006",
url="http://dx.doi.org/10.1016/j.ijdrr.2017.07.006"
}