Selective omission of road features based on mesh density for automatic map generalization

Chen, Jun; Hu, Yungang; Li, Zhilin; Zhao, Rong; Meng, Liqiu

doi:10.1080/13658810802070730

Cited by 60 publications

(49 citation statements)

References 9 publications

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“…In order to properly adjust the position of the crossings (valence ě 3) and dead ends (valence = 1) of the PWs-tbc which are not duplicated to the fromPoint of any CPP, e.g., the nodes p 2 1 and p 4 1 in Figure 3, the local transformation is applied, which employs space partition of the whole conflation area into much smaller regions and therefore can better handle the local distortions in each region. In a road network, the linear topologic structure provides a natural way to spatially subdivide the datasets, i.e., mesh-based partition [21]. A mesh, also called face, can be regarded as a closed region that does not contain any other region.…”

Section: (B) Road Crossings or Dead Ends Which Are Not Duplicated To mentioning

confidence: 99%

Automatic and Accurate Conflation of Different Road-Network Vector Data towards Multi-Modal Navigation

Zhang

Yao

Meng

2016

IJGI

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Abstract:With the rapid improvement of geospatial data acquisition and processing techniques, a variety of geospatial databases from public or private organizations have become available. Quite often, one dataset may be superior to other datasets in one, but not all aspects. In Germany, for instance, there were three major road network vector data, viz. Tele Atlas (which is now "TOMTOM"), NAVTEQ (which is now "here"), and ATKIS. However, none of them was qualified for the purpose of multi-modal navigation (e.g., driving + walking): Tele Atlas and NAVTEQ consist of comprehensive routing-relevant information, but many pedestrian ways are missing; ATKIS covers more pedestrian areas but the road objects are not fully attributed. To satisfy the requirements of multi-modal navigation, an automatic approach has been proposed to conflate different road networks together, which involves five routines: (a) road-network matching between datasets; (b) identification of the pedestrian ways; (c) geometric transformation to eliminate geometric inconsistency; (d) topologic remodeling of the conflated road network; and (e) error checking and correction. The proposed approach demonstrates high performance in a number of large test areas and therefore has been successfully utilized for the real-world data production in the whole region of Germany. As a result, the conflated road network allows the multi-modal navigation of "driving + walking".

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Section: (B) Road Crossings or Dead Ends Which Are Not Duplicated To mentioning

confidence: 99%

Automatic and Accurate Conflation of Different Road-Network Vector Data towards Multi-Modal Navigation

Zhang

Yao

Meng

2016

IJGI

Self Cite

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“…Xu, Liu and Zhang utilized a linear method, which did not rely on the use of semantic data, to select roads based on a hierarchical structure [8]. Chen et al proposed an areal approach to road selection, based on mesh density, which considered the topological, geometric, and semantic properties of road networks [15]. Linear methods have several advantages.…”

Section: Introductionmentioning

confidence: 99%

“…The areal method primarily uses road density as a threshold to control road selection [12,15]. Road density calculation methods include the grid [21], fractal [22], and mesh [15] methods.…”

Section: Introductionmentioning

confidence: 99%

“…Road density calculation methods include the grid [21], fractal [22], and mesh [15] methods. However, these techniques have some limitations.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, road selection is one of the most important steps in road network generalization and a prerequisite that affects its accuracy. Road selection methods can be categorized, according to the network representation mode, into linear methods [4][5][6][7][8][9][10][11] and areal [12][13][14][15] methods.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Hybrid Method for Enhanced Road Feature Selection in Map Generalization

Zhang

Wang

Zhao

2017

IJGI

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Road selection is a critical component of road network generalization that directly affects its accuracy. However, most conventional selection methods are based solely on either a linear or an areal representation mode, often resulting in low selection accuracy and biased structural selection. In this paper we propose an improved hybrid method combining the linear and areal representation modes to increase the accuracy of road selection. The proposed method offers two primary advantages. First, it improves the stroke generation algorithm in a linear representation mode by using an ordinary least square (OLS) model to consider overall information for the roads to be connected. Second, by taking advantage of the areal representation mode, the proposed method partitions road networks and calculates road density based on weighted Voronoi diagrams. Roads were selected using stroke importance and a density threshold. Finally, experiments were conducted comparing the proposed technique with conventional single representation methods. Results demonstrate the increased stroke generation accuracy and improved road selection achieved by this method.

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Generalisation Operators

Stanislawski¹,

Buttenfield²,

Bereuter³

et al. 2014

Lecture Notes in Geoinformation and Cartography

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Selective omission of road features based on mesh density for automatic map generalization

Cited by 60 publications

References 9 publications

Automatic and Accurate Conflation of Different Road-Network Vector Data towards Multi-Modal Navigation

Automatic and Accurate Conflation of Different Road-Network Vector Data towards Multi-Modal Navigation

An Improved Hybrid Method for Enhanced Road Feature Selection in Map Generalization

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