Robust Road Map Inference through Network Alignment of Trajectories

Stanojevic, Rade; Abbar, Sofiane; Thirumuruganathan, Saravanan; Chawla, Sanjay; Filali, Fethi; Aleimat, Ahid

doi:10.1137/1.9781611975321.15

Cited by 51 publications

(40 citation statements)

References 23 publications

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“…For example, the mean-shift algorithm utilized trajectory data to move the seed points until each seed converged on the road centre [13,14]. Analogously, the k-means algorithm was also employed to cluster the trajectory points first [15,16] and produced links among cluster centres by mapping original trajectories to the road graphs. Additionally, the density-based spatial clustering of applications with a noise (DBSCAN) algorithm was utilized to infer road networks by clustering characteristic points [17,18].…”

Section: Related Workmentioning

confidence: 99%

“…First, the distribution of the track points in different regions is not uniform. To infer roads in the sparse density region, we select all the input trajectory points as initial seeds, which is different from the existing clustering methods that use fixed equal-length sampling [15,16] or fixed gridded sampling [14]. • Second, we also need to update the heading direction of each seed while updating its location during each iteration.…”

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confidence: 99%

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

2019

IJGI

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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.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

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

2019

IJGI

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“…Inference from GPS Trajectories. Instead of using aerial imagery, several systems propose to instead infer maps from GPS trajectories [4,5,15]. Each GPS trajectory is a sequence of GPS positions observed as a vehicle moves along the road network during one trip.…”

Section: Related Workmentioning

confidence: 99%

“…This issue has motivated significant interest in automatic map inference. Several systems have been proposed for automatically constructing road maps from aerial imagery [6,11] and GPS trajectories [4,15]. Yet, despite over a decade of research in this space, these systems have not gained traction in OpenStreetMap and other mapping communities.…”

Section: Introductionmentioning

confidence: 99%

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Machine-assisted map editing

Bastani¹,

He²,

Abbar³

et al. 2018

Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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Mapping road networks today is labor-intensive. As a result, road maps have poor coverage outside urban centers in many countries. Systems to automatically infer road network graphs from aerial imagery and GPS trajectories have been proposed to improve coverage of road maps. However, because of high error rates, these systems have not been adopted by mapping communities. We propose machine-assisted map editing, where automatic map inference is integrated into existing, human-centric map editing workflows. To realize this, we build Machine-Assisted iD (MAiD), where we extend the web-based OpenStreetMap editor, iD, with machine-assistance functionality. We complement MAiD with a novel approach for inferring road topology from aerial imagery that combines the speed of prior segmentation approaches with the accuracy of prior iterative graph construction methods. We design MAiD to tackle the addition of major, arterial roads in regions where existing maps have poor coverage, and the incremental improvement of coverage in regions where major roads are already mapped. We conduct two user studies and find that, when participants are given a fixed time to map roads, they are able to add as much as 3.5x more roads with MAiD.

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Automatic Urban Road Network Extraction From Massive GPS Trajectories of Taxis

Gao

Rao

et al. 2021

Handbook of Big Geospatial Data

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Robust Road Map Inference through Network Alignment of Trajectories

Cited by 51 publications

References 23 publications

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

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

Machine-assisted map editing

Automatic Urban Road Network Extraction From Massive GPS Trajectories of Taxis

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