A Semi-Automatic Algorithm on Extracting Road Networks from Airborne LiDAR Data

Feng Li; Xi Min Cui; De Bao Yuan; Kai Long Hu; Wan Yang Xu

doi:10.4028/www.scientific.net/AMR.594-597.2418

Paper Titles

Application of Fuzzy Least Squares Support Vector Machines in Landslide Deformation Prediction
p.2402

Analysis of Landslide Surface Deformation Using Geographically Weighted Regression
p.2406

A Summary of the Scientific Data and its Quality Evaluation
p.2410

Evacuation Simulation Model for Large Hotel Based on GIS
p.2414

A Semi-Automatic Algorithm on Extracting Road Networks from Airborne LiDAR Data
p.2418

The Analysis Model of Coupling Heat Flux in Heavy Oil Reservoir and its Application
p.2425

Analysis of the Instantaneous Local Thermal Equilibrium Assumption for Heat Exchange between Rock Matrix and Fracture Water
p.2430

Study on Heating Zone and Producing Zone of Cyclic Steam Stimulation with Horizontal Well in Heavy Oil Reservoir
p.2438

A Method to Determine the Optimal Water Injection Intensity Based on Oil Production Rate and Water Cut
p.2442

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 594-597A Semi-Automatic Algorithm on Extracting Road...

A Semi-Automatic Algorithm on Extracting Road Networks from Airborne LiDAR Data

Abstract:

Airborne LiDAR (Light Detection and Ranging) is a powerful remote sensing measure to derive 3D coordinates from terrain surface. Automated or semi-automated road networks extraction from laser point clouds has been a challenging work during last few decades. In this paper, combining intensity with height information of point clouds, initial road point class is discerned using few seed points and subsequently smoothed throughout filling holes technology and morphological open operations. The skeleton of road networks is acquired by virtue of image thinning method and further eliminating short road burrs with length criteria. These center points of road are tracked and connected into vector line segments. By simplifying line nodes, road network can be formed in accuracy of which completeness is 79.8% and correctness is 79%. The virtues of this algorithm enables efficiently reduce errors brought by parking lots.

You might also be interested in these eBooks

Advances in Industrial and Civil Engineering

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 594-597)

Pages:

2418-2421

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.594-597.2418

Citation:

Cite this paper

Online since:

November 2012

Authors:

Feng Li, Xi Min Cui, De Bao Yuan, Kai Long Hu, Wan Yang Xu

Keywords:

LiDAR, Point Cloud, Road Extraction, Semi-Automatic

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] A. Alharthy and J. Bethel, "Automated Road Extraction from LIDAR Data," In: Proceedings of ASPRS, Anchorage, Alaska, unpaginated, CD-ROM (2003).

Google Scholar

[2] S. Clode, P. Kootsookos and F. Rottensteiner, "The Automatic Extraction of Roads from LIDAR Data," ISPRS, Istanbul, Turkey, Vol. 34(2004), p.231.

Google Scholar

[3] A. Mancini, E. Frontoni and P. Zingaretti, "Automatic Extraction of Urban Objects from Multi-Source Aerial Data," IAPRS, Paris, France, Vol. 38(2009).

Google Scholar

[4] J. Göpfert and F. Rottensteiner. "Using Building and Bridge Information for Adapting Roads to ALS Data by Means of Network Snakes," IAPRS, Paris, France, Vol.38(2010), p.163.

Google Scholar

[5] S.G. Zhou, S.J. He and H. Li, "Automatic Road Extraction from Lidar Data Based on Height Fitting Difference," LIDAR & RADAR 2011(2011).

DOI: 10.1117/12.912731

Google Scholar

[6] C. Heipke, H. Mayer, C. Wiedemann and O. Jamet: International Archives of Photogrammetry and Remote Sensing, vol. 32(1997), p.151.

Google Scholar