Urban Point Cloud Mining Based on Density Clustering and MapReduce-Reference-Cited by-同舟云学术

Urban Point Cloud Mining Based on Density Clustering and MapReduce

Published:2017-09 Issue:5 Volume:31 Page:04017021
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Aljumaily Harith¹,Laefer Debra F.²,Cuadra Dolores³

Affiliation:

1. Visiting Professor, Dept. of Computer Science and Engineering, Carlos III Univ. of Madrid, Av. Universidad 30, 28911 Madrid, Spain (corresponding author).

2. Professor, School of Civil, Structural, and Environmental Engineering and Earth Institute, Univ. College Dublin, Newstead G25, Belfield, Dublin 4, Ireland; Center for Urban Science and Progress, New York Univ., 1 Metrotech Center, Brooklyn, NY 11201.

3. Associate Professor, Dept. of Computer Science and Engineering, Carlos III Univ. of Madrid, Av. Universidad 30, 28911 Madrid, Spain.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000674

Reference43 articles.

1. Abdullah S. Awrangjeb M. and Lu G. (2014). “Automatic segmentation of LiDAR point cloud data at difference height levels for 3D building extraction.” IEEE Int. Conf. on Multimedia and Expo Workshops IEEE New York 1–6.

2. DATA CLUSTERING

3. Big-data approach for three-dimensional building extraction from aerial laser scanning;Aljumaily H.;J. Comput. Civ. Eng.,2015

4. Automatic extraction of building roofs using LIDAR data and multispectral imagery

5. Applying SR-Tree technique in DBSCAN clustering algorithm;Chakrawarty L.;Int. J. Appl. Innov. Eng. Manage.,2014

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