Measure of Landmark Semantic Salience through Geosocial Data Streams

Quesnot, Teriitutea; Roche, Stéphane

doi:10.3390/ijgi4010001

Cited by 35 publications

(23 citation statements)

References 65 publications

(70 reference statements)

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“…A landmark's semantic salience consists of its historical and cultural significance. So far, semantic salience remains the conceptually most ambiguous category as well as the hardest to address empirically due to the inherently high individual variance (see Quesnot & Roche, 2014, for an overview). Visual salience refers to a landmark's distinctiveness regarding its size, color, and shape as compared to the surrounding environment.…”

Section: Spatial and Perceptual Properties As Determinants Of Landmarmentioning

confidence: 99%

“…However, an automated evaluation is highly desirable if the described problems can be overcome. Finally, we refrained to include an estimation of semantic salience into our research due to the conceptual difficulties to quantify this aspect of a landmark's salience (see Quesnot & Roche, 2014, for a discussion of this issue). Next to the challenges imposed by the conceptualization and quantification of the three individual salience levels individually, future research should attempt to compare their combined influence on spatial learning and knowledge.…”

Section: Potential Limitations and Future Directionsmentioning

confidence: 99%

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Gaze behavior during incidental and intentional navigation in an outdoor environment

Wenczel

Hepperle

Stülpnagel

2016

Spatial Cognition & Computation

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Previous research on landmark selection and route learning derived many of its conclusions from the analysis of memory tasks and verbal route descriptions. We examined the extent to which these findings are reflected in gaze behavior. Wearing a mobile eye tracking device, participants learned the first part of a real-world route incidentally and the second part intentionally. When compared with incidental learning, intentional learning led to a stronger focus on landmarks at structurally salient locations. In contrast, landmarks with a higher level of visual salience attracted generally more fixation time. This finding remained unaffected by learning intention. Our results support the validity of established theoretical frameworks about landmark selection and route learning by extending them to the level of gaze behavior. Additionally, they provide insight into subtle changes of properties determining a landmark's salience as a result of learning intention.

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Section: Spatial and Perceptual Properties As Determinants Of Landmarmentioning

confidence: 99%

Section: Potential Limitations and Future Directionsmentioning

confidence: 99%

Gaze behavior during incidental and intentional navigation in an outdoor environment

Wenczel

Hepperle

Stülpnagel

2016

Spatial Cognition & Computation

View full text Add to dashboard Cite

show abstract

“…Other structural characteristics include the relation of the feature to a road intersection or turning point [6,24,26], as well as distance from the road and the angle in relation to it [27]. Cognitive salience can often be attributed to aspects such as cultural and historic significance [14,15] and activity [28].…”

Section: Characteristics and Detection Of Landmarksmentioning

confidence: 99%

Towards a Landmark-Based Pedestrian Navigation Service Using OSM Data

Rousell

Zipf

2017

IJGI

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Abstract:With the advent of location-aware smartphones, the desire for pedestrian-based navigation services has increased. Unlike car-based services where instructions generally are comprised of distance and road names, pedestrian instructions should instead focus on the delivery of landmarks to aid in navigation. OpenStreetMap (OSM) contains a vast amount of geospatial information that can be tapped into for identifying these landmark features. This paper presents a prototype navigation service that extracts landmarks suitable for navigation instructions from the OSM dataset based on several metrics. This is coupled with a short comparison of landmark availability within OSM, differences in routes between locations with different levels of OSM completeness and a short evaluation of the suitability of the landmarks provided by the prototype. Landmark extraction is performed on a server-side service, with the instructions being delivered to a pedestrian navigation application running on an Android mobile device.

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“…Objects typical of urban environments allow the construction or decomposition of any locality: significant milestones or signals, routes or paths that connect the landmarks and nodes (interchanges or junctions between routes) [38]. Researches in the field of spatial knowledge consider landmarks critical in the interpretation of wayfinding instructions, and consider that adding landmarks in route instructions significantly enhance the wayfinding task, especially for pedestrians [39]. There are even researchers that state that references to street names in route instructions cause significant delays during navigation compared to landmark-based instructions [40,41].…”

Section: Virtual Immersive Environments With Google Street View Geospmentioning

confidence: 99%

Geospatial Google Street View with Virtual Reality: A Motivational Approach for Spatial Training Education

Carrera¹,

Saorín²

2017

IJGI

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Abstract:Motivation is a determining factor in the learning process, and encourages the student to participate in activities that increase their performance. Learning strategies supplemented by computer technology in a scenario-based learning environment can improve students' motivation for spatial knowledge acquisition. In this sense, a workshop carried out with 43-second year engineering students supported by Google Street View mobile geospatial application for location-based tasks is presented, in which participants work in an immersive wayfinding 3D urban environment on virtual reality. Students use their own smartphones with Google Street View application integrated in virtual reality (VR) 3D glasses with a joystick as locomotion interface. The tool to analyse the motivational factor of this pedagogical approach is the multidimensional measurement device Intrinsic Motivation Inventory with six subscales: interest, perceived competence, perceived choice, effort, tension, and value, measured on a seven point Likert scale. Scores in all subscales considered are above 4 on a scale of 7. A usability study conducted at the end of the experiment provides values above 3 on a scale of 5 in efficacy, efficiency and satisfaction. The results of the experiment carried out indicate that geospatial Google Street View application in Virtual Reality is a motivating educational purpose in the field of spatial training.

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Measure of Landmark Semantic Salience through Geosocial Data Streams

Cited by 35 publications

References 65 publications

Gaze behavior during incidental and intentional navigation in an outdoor environment

Gaze behavior during incidental and intentional navigation in an outdoor environment

Towards a Landmark-Based Pedestrian Navigation Service Using OSM Data

Geospatial Google Street View with Virtual Reality: A Motivational Approach for Spatial Training Education

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