automatic and accurate conflation of different road-network vector data towards multi-modal navigation
Clicks: 208
ID: 161294
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
208 views
62 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #54 of 94 articles by views in población y desarrollo
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
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”.
| Reference Key |
zhang2016isprsautomatic
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Meng Zhang;Wei Yao;Liqiu Meng |
| Journal | población y desarrollo |
| Year | 2016 |
| DOI |
10.3390/ijgi5050068
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.