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A novel recursive non-parametric dbscan algorithm for 3d data analysis with an application in rockfall detection
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Metadata
Document Title
A novel recursive non-parametric dbscan algorithm for 3d data analysis with an application in rockfall detection
Author
Dillon P.,Aimmanee P.,Wakai A.,Sato G.,Hung H.V.,Karnjana J.
Name from Authors Collection
Affiliations
Sirindhorn International Institute of Technology, Thammasat University, Phahonyothin Road, Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand; National Electronics and Computer Technology Center (NECTEC), National Science and Technology Development Agency, 112 Thailand Science Park, Khlong Luang, Pathum Thani, 12120, Thailand; Graduate School of Science and Technology, Gunma University, 1-5-1 Tenjin, Kiryu, Gunma, 376-8515, Japan; Graduate School of Environmental Information, Teikyo Heisei University, 4-21-2 Nakano, Nakano, Tokyo, 164-8530, Japan; Faculty of Civil Engineering, Thuyloi University of Vietnam, 175 Tayson Street, Dongda, Hanoi, Hanoi, Viet Nam; Sirindhorn International Institute of Technology, Thammasat University, 131 Moo 5 Tivanont Road, Bangkadi, Meung, Patum Thani, 12000, Thailand
Type
Article
Source Title
Journal of Disaster Research
ISSN
18812473
Year
2021
Volume
16
Issue
4
Page
579-587
Open Access
All Open Access, Hybrid Gold
Publisher
Fuji Technology Press
DOI
10.20965/JDR.2021.P0579
Abstract
The density-based spatial clustering of applications with noise (DBSCAN) algorithm is a well-known algorithm for spatial-clustering data point clouds. It can be applied to many applications, such as crack detection, rockfall detection, and glacier movement detection. Traditional DBSCAN requires two predefined parameters. Suitable values of these parameters depend upon the distribution of the input point cloud. Therefore, estimating these parameters is challenging. This paper proposed a new version of DBSCAN that can automatically customize the parameters. The proposed method consists of two processes: initial parameter estimation based on grid analysis and DBSCAN based on the divide-and-conquer (DC-DBSCAN) approach, which repeatedly performs DBSCAN on each cluster separately and recursively. To verify the proposed method, we applied it to a 3D point cloud dataset that was used to analyze rockfall events at the Puiggcercos cliff, Spain. The total number of data points used in this study was 15,567. The experimental results show that the proposed method is better than the traditional DBSCAN in terms of purity and NMI scores. The purity scores of the proposed method and the traditional DBSCAN method were 96.22% and 91.09%, respectively. The NMI scores of the proposed method and the traditional DBSCAN method are 0.78 and 0.49, respectively. Also, it can detect events that traditional DBSCAN cannot detect. © Fuji Technlogy Press Ltd.
Keyword
3D point cloud | DBSCAN | Divide and conquer | Grid density | Rockfall detection
Industrial Classification
Knowledge Taxonomy Level 1
Knowledge Taxonomy Level 2
Knowledge Taxonomy Level 3
License
CC BY-ND
Rights
Author
Publication Source
Scopus