Matching Networks with Different Levels of Detail

Mustière, Sébastien; Devogèle, Thomas

doi:10.1007/s10707-007-0040-1

Cited by 112 publications

(69 citation statements)

References 18 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Section 2.3.1, or the combination and evolution of them [44]. The two most recent ones are the Delimited-StrokeOriented (DSO) algorithm [44] (see also Section 2.3.3), and NetMatcher [23].…”

Section: Qualitative Comparison Of Gimme and The Rnm-based Approachmentioning

confidence: 99%

“…[23,44]) can be used to establish such a mapping from the source map to the target map. It provides M : N mappings (M ≥ 1, N ≥ 0) of every road segment in the source map, represented by a graph edge, to zero or more road segments in the target map.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding point (ii), notice that e.g. [23] reports a run time of around two hours to match 100,000 road segments on a standard IPC. The time for an additional interactive editing of the results may even take several days.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An approach to geometry-based dynamic location referencing

Ebendt

Tcheumadjeu

2017

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

Introduction An important requirement for knowledge infrastructures in smart cities is the continuous updating of location-based information. Protocols for dynamic location referencing like e.g. OpenLR or AGORA-C tackle the problem of accurately matching locations between dissimilar digital maps. They are map-agnostic and aim at limiting the amount of descriptive data to reduce bandwidth. However, there are applications for which the weaker requirement of map-independence is completely adequate, and for some there are even no restrictions in bandwidth (e.g. in the EC-funded project ROSATTE, and in the DLR projects MobiLind and KeepMoving), and with relaxed constraints it is possible to learn from methods in similar areas like road network matching and map conflation, in order to achieve a more accurate solution. Following this path, this paper presents a map-independent approach developed in the ongoing DLR project I.MoVe, which can be combined with a bandwidth-efficient dynamic location ref- top-down approach, recursively splitting up the source route into parts, thereby following a divide-and-conquer strategy to reduce the problem until it can be solved trivially. Results It is currently capable of mapping closed line locations (i.e. circular routes, representing either the boundaries of areas or the tours of e.g. delivery trucks) from a TeleAtlas map to a NAVTEQ map on-the-fly with a success rate of 97.5% (OpenLR: only 82.5%), and also capable of mapping short line locations (i.e. linear routes) on-the-fly between the same maps, with a success rate of 99.7% (OpenLR: 91.9%). Conclusion In conclusion, the new approach to match linear or circular routes between two dissimilar maps is highly accurate and map-independent, but access to both involved maps is required. The approach can also be combined with a bandwidth-efficient dynamic location referencing method like e.g. OpenLR to obtain a compact format before the descriptive data is transmitted.

show abstract

“…Section 2.3.1, or the combination and evolution of them [44]. The two most recent ones are the Delimited-StrokeOriented (DSO) algorithm [44] (see also Section 2.3.3), and NetMatcher [23].…”

Section: Qualitative Comparison Of Gimme and The Rnm-based Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An approach to geometry-based dynamic location referencing

Ebendt

Tcheumadjeu

2017

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…A lot of advanced matching methods for road networks have been proposed based on geometric, structure and sematic criterions (Cobb et al 1998, Xiong and Sperling 2004, Samal et al 2004, Volz 2006, Mustiè re and Devogele 2007, Zhang 2009). On the other hand, many studies integrated information theory or approaches of graph matching to update the matching criterions iteratively for global optimization.…”

Section: Introductionmentioning

confidence: 99%

Automated Matching Crowdsourcing Road Networks Using Probabilistic Relaxation

Zhang

Wang

Luan

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Geospatial data matching is an important prerequisite for data integration, change detection, and data updating. Presently, crowdsourcing geospatial data is drawing great attention with its significant potential for geospatial data updating and Location Based Services, etc. To explore the availabilities of crowdsourcing geospatial data, the paper proposes a heuristic probabilistic relaxation road matching method, named PRRM. It starts with an initial probabilistic matrix according to geometric dissimilarities and then integrates the relative compatibility coefficient of neighbouring candidate pairs to update the previous matrix. Finally, the initial 1:1 matching pairs are selected based on probabilities calculated and refined based on the structure similarity of the selected matching pairs, then a matching growing process is implemented to find M: N matching pairs. Two experiments between OpenStreetMap and professional data show that our method achieves good performance in matching crowdsourcing and professional data with non-rigid deviations and inconsistent structures. Moreover, the proposed method is independent on matching direction and could handle 1: 0 (Null), 1: M and M: N matching. * zyfwhu@whu.edu.cn

show abstract

“…However, the majority of the current approaches concentrate on network matching and mainly on road networks [18][19][20][21][22][23][24][25][26][27]. In contrast to network matching, there are few studies concerning matching area; nevertheless, despite this shortage of research, there are relevant area-based matching algorithms that must be analyzed.…”

mentioning

confidence: 99%

A Polygon and Point-Based Approach to Matching Geospatial Features

Ruiz-Lendínez

Ureña-Cámara

López

2017

IJGI

View full text Add to dashboard Cite

A methodology for matching bidimensional entities is presented in this paper. The matching is proposed for both area and point features extracted from geographical databases. The procedure used to obtain homologous entities is achieved in a two-step process: The first matching, polygon to polygon matching (inter-element matching), is obtained by means of a genetic algorithm that allows the classifying of area features from two geographical databases. After this, we apply a point to point matching (intra-element matching) based on the comparison of changes in their turning functions. This study shows that genetic algorithms are suitable for matching polygon features even if these features are quite different. Our results show up to 40% of matched polygons with differences in geometrical attributes. With regards to point matching, the vertex from homologous polygons, the function and threshold values proposed in this paper show a useful method for obtaining precise vertex matching.

show abstract

Matching Networks with Different Levels of Detail

Cited by 112 publications

References 18 publications

An approach to geometry-based dynamic location referencing

An approach to geometry-based dynamic location referencing

Automated Matching Crowdsourcing Road Networks Using Probabilistic Relaxation

A Polygon and Point-Based Approach to Matching Geospatial Features

Contact Info

Product

Resources

About