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Open AccessArticle

Road Network Extraction from Low-Frequency Trajectories Based on a Road Structure-Aware Filter

by Daigang Li 1,*, Junhan Li 2 and Juntao Li 3
1
Chongqing Institute of Surveying and Mapping, Ministry of Natural Resources, 10 Tengfang Ave, Chongqing 401120, China
2
School of National Defence Science and Technology, Southwest University of Science and Technology, 59 Qinglong Ave, Mianyang 621010, China
3
School of Civil Engineering and Architecture, Southwest Petroleum University, 59 Xindu Ave, Chengdu 610550, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(9), 374; https://doi.org/10.3390/ijgi8090374
Received: 16 July 2019 / Revised: 22 August 2019 / Accepted: 24 August 2019 / Published: 27 August 2019
(This article belongs to the Special Issue Algorithms and Techniques in Urban Monitoring)
Many studies have utilized global navigation satellite system (such as global positioning system (GPS)) trajectories in order to successfully infer road networks because such data can reveal the geometry and development of a road network, can be obtained in a timely manner, and updated on a low budget. Unfortunately, existing studies for inferring road networks from vehicle traces suffer from low accuracy, especially in dense urban regions and locations with complex structures, such as roundabouts, overpasses, and complex intersections. This study presents a novel two-stage approach for inferring road networks from trajectory points and capturing road geometry with better accuracy. First, a lane structure-aware filter is proposed to cluster vehicle trajectories influenced by high noise and outliers in order to reveal the continuous structure points of lane curves from massive trajectory points. Second, a road tracing operator is utilized to segment the road network geometry by inserting new vertices and segments to a vigorous vertex in the heading of the structure points that are extracted in the first step. Experimental results demonstrate the increased accuracy of the extracted roads and show that the proposed method exhibits strong robustness to noise and various sampling rates. View Full-Text
Keywords: trajectory data mining; linear feature detection; map inference; low-frequency trajectory trajectory data mining; linear feature detection; map inference; low-frequency trajectory
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Li, D.; Li, J.; Li, J. Road Network Extraction from Low-Frequency Trajectories Based on a Road Structure-Aware Filter. ISPRS Int. J. Geo-Inf. 2019, 8, 374.

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