Header

UZH-Logo

Maintenance Infos

Varying salience in indoor landmark selection for familiar and unfamiliar wayfinders: evidence from machine learning and self-reports


Zhou, Zhiyong; Weibel, Robert; Huang, Haosheng (2021). Varying salience in indoor landmark selection for familiar and unfamiliar wayfinders: evidence from machine learning and self-reports. Santa Barbara: UC Santa Barbara: Center for Spatial Studies.

Abstract

For human-centered mobile navigation systems, a computational landmark selection model is critical to automatically include landmarks for communicating routes with users. Although some empirical studies have shown that landmarks selected by familiar and unfamiliar wayfinders, respectively, differ significantly, existing computational models are solely focused on unfamiliar users and ignore selecting landmarks for familiar users, particularly in indoor environments. Meanwhile, it is unclear how the importance of salience metrics employed by machine learning approaches differs from that reported by human participants during landmark selection. In this study, we propose a LambdaMART-based ranking approach to computationally modelling indoor landmark selection. Two models, one for familiar and one for unfamiliar users, respectively, were trained from the human-labelled indoor landmark selection data. The importance of different salience measures in each model was then ranked and compared with human participants’ self-report results of a survey. The evaluation results demonstrate that familiarity does indeed matter in the computational modelling of indoor landmark selection. The ranking differences of salience measures in the trained models show that the salience varies with the familiarity of wayfinders. Moreover, the calculated intraclass correlation coefficients (0.62 for familiar, 0.65 for unfamiliar) illustrate the median consistency between the computational results on feature importance and the self-reported importance results by human participants, confirming the reliability and interpretability of the proposed approach.

Abstract

For human-centered mobile navigation systems, a computational landmark selection model is critical to automatically include landmarks for communicating routes with users. Although some empirical studies have shown that landmarks selected by familiar and unfamiliar wayfinders, respectively, differ significantly, existing computational models are solely focused on unfamiliar users and ignore selecting landmarks for familiar users, particularly in indoor environments. Meanwhile, it is unclear how the importance of salience metrics employed by machine learning approaches differs from that reported by human participants during landmark selection. In this study, we propose a LambdaMART-based ranking approach to computationally modelling indoor landmark selection. Two models, one for familiar and one for unfamiliar users, respectively, were trained from the human-labelled indoor landmark selection data. The importance of different salience measures in each model was then ranked and compared with human participants’ self-report results of a survey. The evaluation results demonstrate that familiarity does indeed matter in the computational modelling of indoor landmark selection. The ranking differences of salience measures in the trained models show that the salience varies with the familiarity of wayfinders. Moreover, the calculated intraclass correlation coefficients (0.62 for familiar, 0.65 for unfamiliar) illustrate the median consistency between the computational results on feature importance and the self-reported importance results by human participants, confirming the reliability and interpretability of the proposed approach.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Downloads

6 downloads since deposited on 14 Oct 2021
6 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Published Research Report
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Date:2021
Deposited On:14 Oct 2021 11:36
Last Modified:14 Oct 2021 11:36
Publisher:UC Santa Barbara: Center for Spatial Studies
Series Name:GIScience 2021 Short Paper Proceedings
OA Status:Green
Publisher DOI:https://doi.org/10.25436/E24S34
Official URL:https://escholarship.org/uc/item/6tt8j58m

Download

Green Open Access

Download PDF  'Varying salience in indoor landmark selection for familiar and unfamiliar wayfinders: evidence from machine learning and self-reports'.
Preview
Content: Published Version
Language: English
Filetype: PDF
Size: 474kB
View at publisher