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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Abstract
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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Details
| Item Type: |
Article
|
| Status: |
Published |
| Creators/Authors: |
|
| 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 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/25411 |
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