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Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approach


Zhou, Zhiyong; Weibel, Robert; Huang, Haosheng (2021). Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approach. International Journal of Geographical Information Science:Epub ahead of print.

Abstract

Landmarks play key roles in human wayfinding and mobile navigation systems. Existing computational landmark selection models mainly focus on outdoor environments, and aim to identify suitable landmarks for guiding users who are unfamiliar with a particular environment, and fail to consider familiar users. This study proposes a familiarity-dependent computational method for selecting suitable landmarks for communicating with familiar and unfamiliar users in indoor environments. A series of salience measures are proposed to quantify the characteristics of each indoor landmark candidate, which are then combined in two LambdaMART-based learning-to-rank models for selecting landmarks for familiar and unfamiliar users, respectively. The evaluation with labelled landmark preference data by human participants shows that people’s familiarity with environments matters in the computational modelling of indoor landmark selection for guiding them. The proposed models outperform state-of-the-art models, and achieve hit rates of 0.737 and 0.786 for familiar and unfamiliar users, respectively. Furthermore, semantic relevance of a landmark candidate is the most important measure for the familiar model, while visual intensity is most informative for the unfamiliar model. This study enables the development of human-centered indoor navigation systems that provide familiarity-adaptive landmark-based navigation guidance.

Abstract

Landmarks play key roles in human wayfinding and mobile navigation systems. Existing computational landmark selection models mainly focus on outdoor environments, and aim to identify suitable landmarks for guiding users who are unfamiliar with a particular environment, and fail to consider familiar users. This study proposes a familiarity-dependent computational method for selecting suitable landmarks for communicating with familiar and unfamiliar users in indoor environments. A series of salience measures are proposed to quantify the characteristics of each indoor landmark candidate, which are then combined in two LambdaMART-based learning-to-rank models for selecting landmarks for familiar and unfamiliar users, respectively. The evaluation with labelled landmark preference data by human participants shows that people’s familiarity with environments matters in the computational modelling of indoor landmark selection for guiding them. The proposed models outperform state-of-the-art models, and achieve hit rates of 0.737 and 0.786 for familiar and unfamiliar users, respectively. Furthermore, semantic relevance of a landmark candidate is the most important measure for the familiar model, while visual intensity is most informative for the unfamiliar model. This study enables the development of human-centered indoor navigation systems that provide familiarity-adaptive landmark-based navigation guidance.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Scopus Subject Areas:Physical Sciences > Information Systems
Social Sciences & Humanities > Geography, Planning and Development
Social Sciences & Humanities > Library and Information Sciences
Uncontrolled Keywords:Library and Information Sciences, Geography, Planning and Development, Information Systems
Language:English
Date:19 July 2021
Deposited On:27 Aug 2021 08:52
Last Modified:28 Aug 2021 20:00
Publisher:Taylor & Francis
ISSN:1365-8816
Additional Information:This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Geographical Information Science, available online: http://wwww.tandfonline.com/10.1080/13658816.2021.1946542.
OA Status:Closed
Publisher DOI:https://doi.org/10.1080/13658816.2021.1946542
Project Information:
  • : FunderUniversity of Zurich Forschungskredit
  • : Grant ID
  • : Project Title

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