Measure of Landmark Semantic Salience through Geosocial Data Streams

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...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Teriitutea Quesnot [verfasserIn] Stéphane Roche [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	automatic landmarks detection systems landmarks landmark semantic salience localness online social networks social location sharing wayfinding

Übergeordnetes Werk:	In: ISPRS International Journal of Geo-Information - MDPI AG, 2012, 4(2014), 1, Seite 31
Übergeordnetes Werk:	volume:4 ; year:2014 ; number:1 ; pages:31

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/ijgi4010001

Katalog-ID:	DOAJ006919804

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callnumber-first	G - Geography, Anthropology, Recreation
author	Teriitutea Quesnot
spellingShingle	Teriitutea Quesnot misc G1-922 misc automatic landmarks detection systems misc landmarks misc landmark semantic salience misc localness misc online social networks misc social location sharing misc wayfinding misc Geography (General) Measure of Landmark Semantic Salience through Geosocial Data Streams
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topic_title	G1-922 Measure of Landmark Semantic Salience through Geosocial Data Streams automatic landmarks detection systems landmarks landmark semantic salience localness online social networks social location sharing wayfinding
topic	misc G1-922 misc automatic landmarks detection systems misc landmarks misc landmark semantic salience misc localness misc online social networks misc social location sharing misc wayfinding misc Geography (General)
topic_unstemmed	misc G1-922 misc automatic landmarks detection systems misc landmarks misc landmark semantic salience misc localness misc online social networks misc social location sharing misc wayfinding misc Geography (General)
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title	Measure of Landmark Semantic Salience through Geosocial Data Streams
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title_full	Measure of Landmark Semantic Salience through Geosocial Data Streams
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title_sort	measure of landmark semantic salience through geosocial data streams
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title_auth	Measure of Landmark Semantic Salience through Geosocial Data Streams
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Measure of Landmark Semantic Salience through Geosocial Data Streams
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