Towards high accuracy road maps generation from massive GPS Traces data

Guo, Tao; Iwamura, Kazuaki; Koga, Masashi

doi:10.1109/igarss.2007.4422884

Cited by 71 publications

(38 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hence, the κ i values range from [0. 5,1]. If the κ i values are close to 1, then the points have more linear distributions, which means that more of the feature points around x i are aligned along a road.…”

Section: Road Skeleton Segmentationmentioning

confidence: 99%

“…Maintaining timely information on road networks [2], especially in developing cities where road networks may change rapidly, is challenging. Conventional ground-based survey map updating techniques are restricted because of the long periods required to collect and organize geographic information [1]. Remote sensing interpretations can be used to extract road maps but cannot capture large-scale and complex road networks, and are affected by occlusions caused by clouds, mist and trees.…”

Section: Introductionmentioning

confidence: 99%

“…Road networks are the basic geographic information that constitute urban structural skeletons and connect various geographic elements in a city, and they play key roles in vehicle navigation, traffic management, and rapid emergency responses [1]. Maintaining timely information on road networks [2], especially in developing cities where road networks may change rapidly, is challenging.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Xing

et al. 2017

IJGI

View full text Add to dashboard Cite

Many studies have used Global Navigation Satellite System (GNSS) traces to successfully extract segments of road networks because such data can be rapidly updated at a low cost. However, most studies have not focused on extracting intersections, which are indispensable parts of road networks in terms of connectivity. However, extracted intersections often present unsatisfactory precision and misleading connectivity. This study proposes a novel method for extracting road intersections from Global Position System (GPS) trace points and for capturing intersections with better accuracy. The key to improving the geometric accuracy of intersections is to identify the dominant orientations of road segments around intersections, merge similar orientations and maintain independent conflicting orientations. Extracting intersections by aligning the dominant orientations can largely reduce location offsets and road distortions. Experiments are performed to demonstrate the increased accuracy and connectivity of extracted road intersections by the proposed method.

show abstract

Section: Road Skeleton Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Xing

et al. 2017

IJGI

View full text Add to dashboard Cite

show abstract

“…In a major departure from prior work, we combine initial density processing as in [9,22,7,21] with subsequent trajectory processing [6,11,24,12,13,3,18]. As shown in [4], density processing holds a significant advantage over trajectory processing in terms of robustness to noise and computational complexity, both supremely important considerations as we grow the amount of trace data used.…”

Section: Topology Refinement ( §6)mentioning

confidence: 99%

“…In order to extract the road map, a second pass is made through these clusters, connecting with road segments those clusters that fall along the path of the raw GPS traces. Methods that belong to this class include algorithms described by Edelkamp and Schrödl [11], Schroedl et al [19], Worrall and Nebot [24], Guo et al [12], Jang et al [13], and Agamennoni et al [3].…”

Section: Related Workmentioning

confidence: 99%

Map inference in the face of noise and disparity

Biagioni

Eriksson

2012

Proceedings of the 20th International Conference on Advances in Geographic Information Systems

176

181

View full text Add to dashboard Cite

This paper describes a process for automatically inferring maps from large collections of opportunistically collected GPS traces. In this type of dataset, there is often a great disparity in terms of coverage. For example, a freeway may be represented by thousands of trips, whereas a residential road may only have a handful of observations. Additionally, while modern GPS receivers typically produce high-quality location estimates, errors over 100 meters are not uncommon, especially near tall buildings or under dense tree coverage. Combined, GPS trace disparity and error present a formidable challenge for the current state of the art in map inference. By tuning the parameters of existing algorithms, a user may choose to remove spurious roads created by GPS noise, or admit less-frequently traveled roads, but not both.In this paper, we present an extensible map inference pipeline, designed to mitigate GPS error, admit less-frequently traveled roads, and scale to large datasets. We demonstrate and compare the performance of our proposed pipeline against existing methods, both qualitatively and quantitatively, using a real-world dataset that includes both high disparity and noise. Our results show significant improvements over the current state of the art.

show abstract

Integrating Sensors and Social Networks

Aggarwal¹,

Abdelzaher

2011

Social Network Data Analytics

View full text Add to dashboard Cite

Towards high accuracy road maps generation from massive GPS Traces data

Cited by 71 publications

References 2 publications

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Map inference in the face of noise and disparity

Integrating Sensors and Social Networks

Contact Info

Product

Resources

About