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.
|Title of host publication||Computing in Civil Engineering 2019|
|Subtitle of host publication||Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019|
|Editors||Yong K. Cho, Fernanda Leite, Amir Behzadan, Chao Wang|
|Publisher||American Society of Civil Engineers (ASCE)|
|Number of pages||8|
|Publication status||Published - 2019|
|Event||ASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019 - Atlanta, United States|
Duration: 2019 Jun 17 → 2019 Jun 19
|Name||Computing in Civil Engineering 2019: Data, Sensing, and Analytics - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019|
|Conference||ASCE International Conference on Computing in Civil Engineering 2019: Data, Sensing, and Analytics, i3CE 2019|
|Period||19/6/17 → 19/6/19|
Bibliographical noteFunding 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).
© 2019 American Society of Civil Engineers.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Civil and Structural Engineering