Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/ijgi6070196 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION |
Keywords | Field | DocType |
map generalization,road selection,improved hybrid algorithm,road network partition | Road networks,Feature selection,Computer science,Ordinary least squares,Linear representation,Algorithm,Voronoi diagram,Cartographic generalization,Artificial intelligence,Machine learning | Journal |
Volume | Issue | Citations |
6 | 7 | 3 |
PageRank | References | Authors |
0.38 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianchen Zhang | 1 | 5 | 1.10 |
| Yanhui Wang | 2 | 16 | 6.74 |
| Wenji Zhao | 3 | 25 | 17.51 |