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Automatically Tracking Road Centerlines from Low-Frequency GPS Trajectory Data

Authors :
Banqiao Chen
Chibiao Ding
Wenjuan Ren
Guangluan Xu
Source :
ISPRS International Journal of Geo-Information, Vol 10, Iss 3, p 122 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

High-quality digital road maps are essential prerequisites of location-based services and smart city applications. The massive and accessible GPS trajectory data generated by mobile GPS devices provide a new means through which to generate maps. However, due to the low sampling rate and multi-level disparity problems, automatically generating road maps is challenging and the generated maps cannot yet meet commercial requirements. In this paper, we present a GPS trajectory data-based road tracking algorithm, including an active contour-based road centerline refinement algorithm as the necessary post-processing. First, the low-frequency trajectory data were transferred into a density estimation map representing the roads through a kernel density estimator, for a seeding algorithm to automatically generate the initial points of the road-tracking algorithm. Then, we present a template-matching-based road-direction extraction algorithm for the road trackers to conduct simple correction, based on local density information. Last, we present an active contour-based road centerline refinement algorithm, considering both the geometric information of roads and density information. The generated road map was quantitatively evaluated using maps offered by the OpenStreetMap. Compared to other methods, our approach could produce a higher quality map with fewer zig-zag roads, and therefore more accurately represents reality.

Details

Language :
English
ISSN :
22209964
Volume :
10
Issue :
3
Database :
Directory of Open Access Journals
Journal :
ISPRS International Journal of Geo-Information
Publication Type :
Academic Journal
Accession number :
edsdoj.71ecc06e0ad413e813aa6ce7e0a1acf
Document Type :
article
Full Text :
https://doi.org/10.3390/ijgi10030122