B-EagleV: Visualization of Big Point Cloud Datasets in Civil Engineering Using a Distributed Computing Solution
Publication: Journal of Computing in Civil Engineering
Volume 36, Issue 3
Abstract
Point cloud data (PCD) have attracted attention in many disciplines, including civil engineering. However, big PCD have posed great challenges for conventional approaches using a single computer. Although many published studies have demonstrated distributed computing’s potential for large-scale data-intensive applications, this technology has not been applied widely in processing of big PCD due to a lack of methods for data management, visualization, and analysis. To strengthen the foundation of distributed computation in civil engineering, this study offers a solution to one of the obstacles presented in the previous studies, which was the visualization of big PCD. The practical result of this study is the introduction of B-EagleV, a cost-effective Hadoop-based solution for the visualization of big PCD in civil engineering with almost complete components of scalable storage, high-performance rendering, and interactive visualization. Through experiment results and demonstration, B-EagleV showed great promise for data management, progress monitoring, and survey conduction in the construction sector.
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Data Availability Statement
Some data used during the study are available in a repository online in accordance with funder data retention policies (City of Melbourne Open Data Team 2018).
Acknowledgments
This work was supported by a National Research Foundation of Korea (NRF) grant (No. 2018R1A2B2009160) funded by the Korean government (Ministry of Science and ICT).
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Journal of Computing in Civil Engineering
Volume 36 • Issue 3 • May 2022
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© 2022 American Society of Civil Engineers.
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Received: Sep 19, 2021
Accepted: Jan 3, 2022
Published online: Feb 28, 2022
Published in print: May 1, 2022
Discussion open until: Jul 28, 2022
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