Development of Massive Point Cloud Data Geoprocessing Framework for Construction Site Monitoring

Minh Hieu Nguyen, Sanghyun Yoon, Sangyoon Park, Joon Heo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In civil engineering, point cloud data are used for construction site monitoring. However, with such massive datasets, processing these data have barriers due to the size and computational intensity. This study presents the first scalable Hadoop based point cloud management and processing framework to overcome those barriers. The framework consists of 3 layers: (1) storage layer; (2) operation layer; (3) interactive layer. The storage layer is optimized by indexing techniques to accelerate the map-reduce applications. The operation layer is equipped not only with the common operations such as range query, kNN, or spatial join, but also with two powerful modules including "Change Detection"and "3D Geometric Model", for efficient monitoring. The Interactive layer enables users to approach a parallel processing model on Hadoop without requiring deep related knowledge. In addition, this layer could also visualize massive point cloud data directly from Hadoop, which is useful for analytical processes. With such components, our framework is scalable, inexpensive, and full-fledged.

Original languageEnglish
Title of host publicationComputing in Civil Engineering 2019
Subtitle of host publicationData, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
EditorsYong K. Cho, Fernanda Leite, Amir Behzadan, Chao Wang
PublisherAmerican Society of Civil Engineers (ASCE)
Pages185-192
Number of pages8
ISBN (Electronic)9780784482438
Publication statusPublished - 2019
EventASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019 - Atlanta, United States
Duration: 2019 Jun 172019 Jun 19

Publication series

NameComputing in Civil Engineering 2019: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019

Conference

ConferenceASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019
CountryUnited States
CityAtlanta
Period19/6/1719/6/19

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. 2018R1A2B2009160).

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Civil and Structural Engineering

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