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 t
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Thus, we are asking the following global research question as a starting point: can we improve the urban intelligence using geosocial data generated by users of online social networks? We argue that geolocated content published on Facebook and Swarm can be exploited to enhance citizens’ spatial literacy. More precisely, check-ins datasets can be used to improve human wayfinding and smart mobility by detecting relevant semantic landmarks. Lots of research in wayfinding is done in order to enable individuals to reach as quickly as possible a desired destination, to help people with disabilities by designing cognitively appropriate orientation signs, and reduce the fact of being lost . Therefore, designing tools that effectively support people’s wayfinding remains a major concern.
In order to defend our argument, we detail in the following section a brief state of art related to the concept of wayfinding. Then, we focus both on landmarks and systems designed for their automatic detection. The fourth section puts forward the reasons why check-ins are, in our opinion, a reliable source of information to identify semantic landmarks. More precisely, three scores based on Facebook and Swarm check-ins are suggested in order to measure landmark semantic salience. Finally, the last section of this article presents concrete examples where these scores are applied with real check-ins datasets harvested from Facebook and Foursquare APIs.