Next Article in Journal
Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap
Previous Article in Journal
Toward the Development of a Marine Administration System Based on International Standards
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(7), 196; doi:10.3390/ijgi6070196

An Improved Hybrid Method for Enhanced Road Feature Selection in Map Generalization

1,2,3,* and 1,2,3
College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
3D Information Collection and Application Key Lab of Education Ministry, Capital Normal University, Beijing 100048, China
Beijing State key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China
Author to whom correspondence should be addressed.
Academic Editors: Marinos Kavouras and Wolfgang Kainz
Received: 20 April 2017 / Revised: 28 June 2017 / Accepted: 28 June 2017 / Published: 1 July 2017
View Full-Text   |   Download PDF [4536 KB, uploaded 3 July 2017]   |  


Road selection is a critical component of road network generalization that directly affects its accuracy. However, most conventional selection methods are based solely on either a linear or an areal representation mode, often resulting in low selection accuracy and biased structural selection. In this paper we propose an improved hybrid method combining the linear and areal representation modes to increase the accuracy of road selection. The proposed method offers two primary advantages. First, it improves the stroke generation algorithm in a linear representation mode by using an ordinary least square (OLS) model to consider overall information for the roads to be connected. Second, by taking advantage of the areal representation mode, the proposed method partitions road networks and calculates road density based on weighted Voronoi diagrams. Roads were selected using stroke importance and a density threshold. Finally, experiments were conducted comparing the proposed technique with conventional single representation methods. Results demonstrate the increased stroke generation accuracy and improved road selection achieved by this method. View Full-Text
Keywords: map generalization; road selection; improved hybrid algorithm; road network partition map generalization; road selection; improved hybrid algorithm; road network partition

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhang, J.; Wang, Y.; Zhao, W. An Improved Hybrid Method for Enhanced Road Feature Selection in Map Generalization. ISPRS Int. J. Geo-Inf. 2017, 6, 196.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top