Paper details

Title: Measuring Landmark Salience in Rural Areas: A Comparative Study of Two Models

Authors: Eva Nuhn, Michaela Ehrig, Leonie Engemann, Fabian Heißerer, Julia Maltan, Jenny Ruf, Sabine Timpf

Abstract: Obtained from CrossRef

Abstract. A number of measures have been developed to formally define the landmark salience of a geographic feature in order to identify landmarks for inclusion in pedestrian navigation systems. However, most of the available research has focused on urban environments. Rural areas differ from urban environments in several ways, such as their level of structure and regularity, and the type and density of landmarks. In this paper, we focus on measures for calculating the landmark salience of geographic features in rural areas. We present results from two models: one explicitly designed to identify landmarks in rural areas, and one originally focused on urban areas. We compare the identified landmarks with those from a survey. The results show that the differences between the models and the survey results are not statistically significant. The results of the two models are marginally statistically significantly different. The first model identifies highly semantically salient geographic features as landmarks, while the second model does not consider semantic salience and prefers natural geographic features. Investigating these facts further may be part of future research.

Codecheck details

Certificate identifier: 2025-008

Codechecker name: Daniel Nüst

Time of codecheck: 2025-06-06 14:00:00

Repository: https://github.com/reproducible-agile/reviews-2025|reports/08

Codecheck report: https://doi.org/10.53962/wxh7-8yjd

Summary:

The reproduction of the visualisations and statistics presented in the manuscript was successful. The authors provided a link to an online repo with several data files which seem to have been the basis for the illustrative map in the paper, as well as a spreadsheet which includes the model calculations, plots, and statistical tests presented in the paper.

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© Stephen Eglen & Daniel Nüst

Published under CC BY-SA 4.0

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