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Automatic selection of landmarks for navigation guidance

Zhu, R and Karimi, HA (2015) Automatic selection of landmarks for navigation guidance. Transactions in GIS, 19 (2). 247 - 261. ISSN 1361-1682

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Although current navigation services provide significant benefits to people's mobility, the turn-by-turn instructions they provide are sometimes ineffective. These instructions require people to maintain a high level of attention and cognitive workload while performing distance or angle measurements on their own mental map. To overcome this problem, landmarks have been identified as playing a major role in turn-by-turn instructions. This requires the availability of landmarks in navigation databases. Landmarks are commonly selected manually, which involves time-consuming and tedious tasks. Automatic selection of landmarks has recently gained the attention of researchers but currently there are only a few techniques that can select appropriate landmarks. In this article, we present a technique based on a neural network model, where both static and dynamic features are used for selecting landmarks automatically. To train and test this model, two labeling approaches, manual labeling and rule-based labeling, are also discussed. Experiments on the developed technique were conducted and the results show that rule-based labeling has a precision of approximately 90%, which makes the technique suitable and reliable for automatic selection of landmarks.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Zhu, R
Karimi, HAhkarimi@pitt.eduHKARIMI0000-0001-5331-5004
Date: 1 April 2015
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Transactions in GIS
Volume: 19
Number: 2
Page Range: 247 - 261
DOI or Unique Handle: 10.1111/tgis.12095
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 1361-1682
Date Deposited: 19 Jun 2015 16:23
Last Modified: 30 Mar 2021 11:55


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