Next Article in Journal
Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms
Next Article in Special Issue
Automatic In Situ Calibration of a Spinning Beam LiDAR System in Static and Kinematic Modes
Previous Article in Journal
An Evaluation of Different Training Sample Allocation Schemes for Discrete and Continuous Land Cover Classification Using Decision Tree-Based Algorithms
Previous Article in Special Issue
Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(8), 9682-9704;

Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data

1,2,3,* , 1,2,3
1,2,* and 1,2
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, China
Department of Geographic Information Science, Nanjing University, Nanjing 210093, China
Collaborative Innovation Center for the South Sea Studies, Nanjing University, Nanjing 210093, China
Authors to whom correspondence should be addressed.
Academic Editors: Juha Hyyppä and Prasad S. Thenkabail
Received: 23 May 2015 / Revised: 23 July 2015 / Accepted: 27 July 2015 / Published: 30 July 2015
(This article belongs to the Special Issue Lidar/Laser Scanning in Urban Environments)
Full-Text   |   PDF [2054 KB, uploaded 30 July 2015]   |  


Change detection is a major issue for urban area monitoring. In this paper, a new three-step point-based method for detecting changes to buildings and trees using airborne light detection and ranging (LiDAR) data is proposed. First, the airborne LiDAR data from two dates are accurately registered using the iterative closest point algorithm, and a progressive triangulated irregular network densification filtering algorithm is used to separate ground points from non-ground points. Second, an octree is generated from the non-ground points to store and index the irregularly-distributed LiDAR points. Finally, by comparing the LiDAR points from two dates and using the AutoClust algorithm, those areas of buildings and trees in the urban environment that have changed are determined effectively and efficiently. The key contributions of this approach are the development of a point-based method to effectively solve the problem of objects at different scales, and the establishment of rules to detect changes in buildings and trees to urban areas, enabling the use of the point-based method over large areas. To evaluate the proposed method, a series of experiments using aerial images are conducted. The results demonstrate that satisfactory performance can be obtained using the proposed approach. View Full-Text
Keywords: change detection; octree; airborne LiDAR; buildings; trees; urban environment change detection; octree; airborne LiDAR; buildings; trees; urban environment

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Xu, H.; Cheng, L.; Li, M.; Chen, Y.; Zhong, L. Using Octrees to Detect Changes to Buildings and Trees in the Urban Environment from Airborne LiDAR Data. Remote Sens. 2015, 7, 9682-9704.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top