Landmarks: GIScience for Intelligent Services
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Biological systems consolidate spatial knowledge into three interconnected forms: landmarks, route knowledge, and survey knowledge.
“Robust biological systems consolidate spatial knowledge into three interconnected forms: landmarks for salient cues, route knowledge for movement trajectories, and survey knowledge for map-like representations.”
From Brain-inspired spatial intelligence for embodied agents (2026) · cites this paper as [23]
Landmarks are not communicated effectively on mobile navigation systems to support wayfinders' spatial learning.
“Despite their acknowledged contribution to various components of spatial navigation and human cognition (see reviews by Chan et al., 2012; Epstein & Vass, 2014; Epstein et al., 2017; Yesiltepe et al., 2021), landmarks are not communicated effectively on mobile navigation systems to support wayfinders’ spatial learning (Richter & Winter, 2014).”
From The influence of landmark visualization style on task performance, visual attention, and spatial learning in a real-world navigation task (2024) · cites this paper as Richter & Winter, 2014
The means by which to effectively communicate landmark information on mobile navigation systems have yet to be established.
“So, whilst the benefit of landmark information for efficient navigation is clear, the means by which to effectively communicate that information on mobile navigation systems have yet to be established (Richter & Winter, 2014).”
From The influence of landmark visualization style on task performance, visual attention, and spatial learning in a real-world navigation task (2024) · cites this paper as Richter & Winter, 2014
Previous work shows that visually salient landmarks attract and modulate viewers' bottom-up or stimulus-driven visual attention, but in different ways for individuals with different backgrounds, training, and abilities.
“This finding aligns with previous work in that visually salient landmarks do attract and modulate viewers’ bottom-up or stimulus-driven visual attention, but clearly in different ways for individuals with different backgrounds, training, abilities, etc (Richter & Winter, 2014; Wenczel et al., 2017; Wolfe & Horowitz, 2017; Yesiltepe et al., 2021).”
From The influence of landmark visualization style on task performance, visual attention, and spatial learning in a real-world navigation task (2024) · cites this paper as Richter & Winter, 2014
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“So, whilst the benefit of landmark information for efficient navigation is clear, the means by which to effectively communicate that information on mobile navigation systems have yet to be established (Richter & Winter, 2014).”
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Self-citation
- Authors
- Richter, K.-F., & Winter, S.
- Affiliations
- The University of Melbourne (AU)
- Venue
- Springer eBooks
- Publisher
- Springer International Publishing
- Published
- 2014
- OpenAlex
- W4205511608
- Cited by
- 133
- References
- 9
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 11 · 2023: 15 · 2022: 11 · 2021: 11 · 2020: 13 · 2019: 13 · 2018: 6 · 2017: 16 · 2016: 18 · 2015: 8 · 2014: 1
- FWCI
- 5.58
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer eBooks (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Social Sciences › Geography, Planning and Development › Geographic Information Systems Studies
- Physical Sciences › Engineering › Building and Construction › Urban Design and Spatial Analysis
- Keywords
- landmarks, geographic information science, landmark selection, cognitive systems, human-computer interaction, navigation services, cognitive modeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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