Continuous Road Network Generalization throughout All Scales

Šuba, Radan; Meijers, Martijn; Oosterom, Peter van

doi:10.3390/ijgi5080145

Cited by 18 publications

(15 citation statements)

References 21 publications

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“…When a value of this parameter is given, we can obtain a level detail of buildings (a generalized result) from the clusters whose maximal distances are no more than this value. Thus, the links among building clusters, generalized objects, and scale parameter values can be stored intrinsically; these links are often missing in multi-scale representations [38]. Figures 12 and 13 present an example of how these links are derived.…”

Section: Resultsmentioning

confidence: 99%

Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling Subgroups

Zhang

Yang

2018

IJGI

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Map generalization utilizes transformation operations to derive smaller-scale maps from larger-scale maps, and is a key procedure for the modelling and understanding of geographic space. Studies to date have largely applied a fixed tolerance to aggregate clustered buildings into a single object, resulting in the loss of details that meet cartographic constraints and may be of importance for users. This study aims to develop a method that amalgamates clustered buildings gradually without significant modification of geometry, while preserving the map details as much as possible under cartographic constraints. The amalgamation process consists of three key steps. First, individual buildings are grouped into distinct clusters by using the graph-based spatial clustering application with random forest (GSCARF) method. Second, building clusters are decomposed into scaling subgroups according to homogeneity with regard to the mean distance of subgroups. Thus, hierarchies of building clusters can be derived based on scaling subgroups. Finally, an amalgamation operation is progressively performed from the bottom-level subgroups to the top-level subgroups using the maximum distance of each subgroup as the amalgamating tolerance instead of using a fixed tolerance. As a consequence of this step, generalized intermediate scaling results are available, which can form the multi-scale representation of buildings. The experimental results show that the proposed method can generate amalgams with correct details, statistical area balance and orthogonal shape while satisfying cartographic constraints (e.g., minimum distance and minimum area).

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Section: Resultsmentioning

confidence: 99%

Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling Subgroups

Zhang

Yang

2018

IJGI

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“…In this subsection, two indices were selected as quality measures of the extracted roads. The first index was the road's length [40,41]. Assume Lr is the length of the real road and Lg is the length of the road used for quality evaluation.…”

Section:  Geometric Evaluation Of Generated Centerlines Compared Witmentioning

confidence: 99%

A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data

Wu³

2019

IJGI

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With the rapid development of cities, the geographic information of urban blocks is also changing rapidly. However, traditional methods of updating road data cannot keep up with this development because they require a high level of professional expertise for operation and are very time-consuming. In this paper, we develop a novel method for extracting missing roadways by reconstructing the topology of the roads from big mobile navigation trajectory data. The three main steps include filtering of original navigation trajectory data, extracting the road centerline from navigation points, and establishing the topology of existing roads. First, data from pedestrians and drivers on existing roads were deleted from the raw data. Second, the centerlines of city block roads were extracted using the RSC (ring-stepping clustering) method proposed herein. Finally, the topologies of missing roads and the connections between missing and existing roads were built. A complex urban block with an area of 5.76 square kilometers was selected as the case study area. The validity of the proposed method was verified using a dataset consisting of five days of mobile navigation trajectory data. The experimental results showed that the average absolute error of the length of the generated centerlines was 1.84 m. Comparative analysis with other existing road extraction methods showed that the F-score performance of the proposed method was much better than previous methods.

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“…Li and Hence, it is necessary to generate a weighted Voronoi diagram to partition the road network according to the importance of a road segment. Road segment importance is calculated using Equation (2). The generation algorithm of a weighted Voronoi diagram was developed by [28].…”

Section: Road Density Based On a Weighted Voronoi Diagrammentioning

confidence: 99%

“…As a result, road network generalization has become an active area of research in map generalization [1][2][3]. Furthermore, road selection is one of the most important steps in road network generalization and a prerequisite that affects its accuracy.…”

Section: Introductionmentioning

confidence: 99%

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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Continuous Road Network Generalization throughout All Scales

Cited by 18 publications

References 21 publications

Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling Subgroups

Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling Subgroups

A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data

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

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