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Open AccessArticle

Road Map Inference: A Segmentation and Grouping Framework

Department of Geomatics Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
Author to whom correspondence should be addressed.
Academic Editors: Georg Gartner, Haosheng Huang and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(8), 130;
Received: 4 May 2016 / Revised: 9 July 2016 / Accepted: 14 July 2016 / Published: 23 July 2016
(This article belongs to the Special Issue Location-Based Services)
PDF [1867 KB, uploaded 23 July 2016]


We propose a new segmentation and grouping framework for road map inference from GPS traces. We first present a progressive Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm with an orientation constraint to partition the whole point set of the traces into clusters that represent road segments. A new point cluster grouping algorithm, according to the topological relationship and spatial proximity of the point clusters to recover the road network, is then developed. After generating the point clusters, the robust Locally-Weighted Scatterplot Smooth (Lowess) method is used to extract their centerlines. We then propose to build the topological relationship of the centerlines by a Hidden Markov Model (HMM)-based map matching algorithm; and to assess whether the spatial proximity between point clusters by assuming the distances from the points to the centerline comply with a Gaussian distribution. Finally, the point clusters are grouped according to their topological relationship and spatial proximity to form strokes for recovering the road map. Experimental results show that our algorithm is robust to noise and varied sampling rates. The generated road maps show high geometric accuracy. View Full-Text
Keywords: map inference; DBSCAN; HMM map matching; 2D point cloud segmentation; point cluster grouping map inference; DBSCAN; HMM map matching; 2D point cloud segmentation; point cluster grouping

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Qiu, J.; Wang, R. Road Map Inference: A Segmentation and Grouping Framework. ISPRS Int. J. Geo-Inf. 2016, 5, 130.

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