Document Type
Article
Publication Date
11-2019
Publication Source
Automation In Construction
Abstract
Although the benefit of participatory sensing for collecting local data over large areas has long been recognized, it has not been widely used for various applications such as disaster preparation due to a lack of geospatial localization capability with respect to a distant object. In such applications, objects of interest need to be ro- bustly localized and documented for supporting data-driven decision-making in site inspection and resource mobilization. However, participatory sensing is inappropriate to localize a distant object due to the absence of ranging sensors in citizens' mobile devices; thus, the localization accuracy varies to a large extent. To address this issue, this study presents a novel geospatial localization method for distant objects based on participatory sensing. The proposed geospatial localization process consists of multiple computational modules—a geographic coordinate conversion, mean-shift clustering, deep learning-based object detection, magnetic declination ad- justment, line of sight equation formulation, and the Moore-Penrose generalized inverse method—to improve the localization accuracy in participatory sensing environments. The experiments were conducted in Houston and College Station in Texas to evaluate the proposed method, and the experimental results demonstrated a reasonable localization accuracy, recording the distance errors of 1.5 m to 27.8 m when the distance from ob- servers to the objects of interest were 17 m to 296 m. The proposed method is expected to contribute to rapid data collection over large urban areas, thereby facilitating disaster preparedness that needs to identify locations of distant objects at risk.
ISBN/ISSN
0926-5805
Document Version
Published Version
Publisher
Elsevier
Volume
107
Peer Reviewed
yes
eCommons Citation
Kim, Hongjo and Ham, Youngjib, "Participatory Sensing-Based Geospatial Localization of Distant Objects for Disaster Preparedness in Urban Built Environments" (2019). Civil and Environmental Engineering and Engineering Mechanics Faculty Publications. 73.
https://ecommons.udayton.edu/cee_fac_pub/73
Included in
Civil Engineering Commons, Construction Engineering and Management Commons, Environmental Engineering Commons, Other Civil and Environmental Engineering Commons, Transportation Engineering Commons
Comments
This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1016/j.autcon.2019.102960