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Article

Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data

by 1,2,3, 1,3,4,*, 1,2 and 1,2
1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
Key Laboratory of Network Information System Technology (NIST), Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
3
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
4
National Key Laboratory of Science and Technology on Microwave Imaging, Beijing 100190, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(3), 181; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9030181
Received: 19 February 2020 / Revised: 19 March 2020 / Accepted: 22 March 2020 / Published: 24 March 2020
The requirements of location-based services have generated an increasing need for up-to-date digital road maps. However, traditional methods are expensive and time-consuming, requiring many skilled operators. The feasibility of using massive GPS trajectory data provides a cheap and quick means for generating and updating road maps. The detection of road intersections, being the critical component of a road map, is a key problem in map generation. Unfortunately, low sampling rates and high disparities are ubiquitous among floating car data (FCD), making road intersection detection from such GPS trajectories very challenging. In this paper, we extend a point clustering-based road intersection detection framework to include a post-classification course, which utilizes the geometric features of road intersections. First, we propose a novel turn-point position compensation algorithm, in order to improve the concentration of selected turn-points under low sampling rates. The initial detection results given by the clustering algorithm are recall-focused. Then, we rule out false detections in an extended classification course based on an image thinning algorithm. The detection results of the proposed method are quantitatively evaluated by matching with intersections from OpenStreetMap using a variety of distance thresholds. Compared with other methods, our approach can achieve a much higher recall rate and better overall performance, thereby better supporting map generation and other similar applications. View Full-Text
Keywords: road intersections; GPS trajectory; low-frequency trajectory data; map generation road intersections; GPS trajectory; low-frequency trajectory data; map generation
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MDPI and ACS Style

Chen, B.; Ding, C.; Ren, W.; Xu, G. Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data. ISPRS Int. J. Geo-Inf. 2020, 9, 181. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9030181

AMA Style

Chen B, Ding C, Ren W, Xu G. Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data. ISPRS International Journal of Geo-Information. 2020; 9(3):181. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9030181

Chicago/Turabian Style

Chen, Banqiao, Chibiao Ding, Wenjuan Ren, and Guangluan Xu. 2020. "Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data" ISPRS International Journal of Geo-Information 9, no. 3: 181. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9030181

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