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ISPRS Int. J. Geo-Inf. 2015, 4(1), 1-31; doi:10.3390/ijgi4010001

Measure of Landmark Semantic Salience through Geosocial Data Streams

Center for Research in Geomatics, Université Laval, 1055 Avenue du Séminaire, Pavillon Louis-Jacques Casault, Québec (QC) G1V 0A6, Canada
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Author to whom correspondence should be addressed.
Academic Editors: Alexander Zipf and Wolfgang Kainz
Received: 19 July 2014 / Accepted: 17 December 2014 / Published: 30 December 2014
(This article belongs to the Special Issue Geoweb 2.0)
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Abstract

Research in the area of spatial cognition demonstrated that references to landmarks are essential in the communication and the interpretation of wayfinding instructions for human being. In order to detect landmarks, a model for the assessment of their salience has been previously developed by Raubal and Winter. According to their model, landmark salience is divided into three categories: visual, structural, and semantic. Several solutions have been proposed to automatically detect landmarks on the basis of these categories. Due to a lack of relevant data, semantic salience has been frequently reduced to objects’ historical and cultural significance. Social dimension (i.e., the way an object is practiced and recognized by a person or a group of people) is systematically excluded from the measure of landmark semantic salience even though it represents an important component. Since the advent of mobile Internet and smartphones, the production of geolocated content from social web platforms—also described as geosocial data—became commonplace. Actually, these data allow us to have a better understanding of the local geographic knowledge. Therefore, we argue that geosocial data, especially Social Location Sharing datasets, represent a reliable source of information to precisely measure landmark semantic salience in urban area. View Full-Text
Keywords: automatic landmarks detection systems; landmarks; landmark semantic salience; localness; online social networks; social location sharing; wayfinding automatic landmarks detection systems; landmarks; landmark semantic salience; localness; online social networks; social location sharing; wayfinding
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Quesnot, T.; Roche, S. Measure of Landmark Semantic Salience through Geosocial Data Streams. ISPRS Int. J. Geo-Inf. 2015, 4, 1-31.

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