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Open AccessArticle

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

by 1,2,*, 2,* and 2
1
School of Human Settlements and Civil Engineering, Xi’An Jiaotong University, Xi’an 710049, China
2
Institute of Photogrammetry and Cartography, Technische Universität München, 80333 Munich, Germany
*
Authors to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(5), 68; https://doi.org/10.3390/ijgi5050068
Received: 3 February 2016 / Revised: 29 April 2016 / Accepted: 9 May 2016 / Published: 16 May 2016
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”. View Full-Text
Keywords: data conflation; pedestrian ways; multi-modal navigation data conflation; pedestrian ways; multi-modal navigation
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Zhang, M.; Yao, W.; Meng, L. Automatic and Accurate Conflation of Different Road-Network Vector Data towards Multi-Modal Navigation. ISPRS Int. J. Geo-Inf. 2016, 5, 68.

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