Automatic Landmark Identification in Large Virtual Environment: A Spatial Data Mining Approach

Lazern, S.Y.; Sheta, Walaa M.

doi:10.1109/iv.2005.25

Cited by 9 publications

(13 citation statements)

References 9 publications

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“…Indeed, within wayfinding and navigation tasks, the use of landmarks has been found to be a key aspect [5,6], and a large amount of research has been conducted as to how landmarks are used in navigational tasks [4,7,8] with a distinct importance being found in relation to routing descriptions [6,9,10]. In particular, much research has emerged relating to pedestrian-based navigation [11][12][13][14][15][16], where the use of landmarks can be seen as highly beneficial.…”

Section: Landmarks In Navigationmentioning

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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Section: Landmarks In Navigationmentioning

confidence: 99%

Towards a Landmark-Based Pedestrian Navigation Service Using OSM Data

Rousell

Zipf

2017

IJGI

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“…Another group of researchers designed a methodology based on spatial outlier detection to find landmarks in urban settings (Lazem & Sheta, 2005). Their methodology considers the following building attributes: height, width, color, type of building, and proximity to corners.…”

Section: Related Researchmentioning

confidence: 99%

“…Simulation Experiments-Used to show that the proposed landmark selection technique can detect salient buildings (Elias, 2003a(Elias, , 2003bLazem & Sheta, 2005;Raubal & Winter, 2002). b. Saliency Verification Experiments-Used to show that human subjects and the proposed approach usually agree on which one of the buildings in a neighborhood should be designated as the landmark .…”

Section: Sgmentioning

confidence: 99%

“…Table 2 shows the 11 attributes that were analyzed, and the range for the attribute values used in the simulation. Table 3 shows possible values for the Type attribute (Lazem and Sheta, 2005).…”

Section: Dynamic Attributesmentioning

confidence: 99%

“…In fact, there have even been several different methods proposed to automatically identify the landmarks in large-scale environments (Elias, 2003a(Elias, , 2003bLazem & Sheta, 2005;Nothegger et al, 2004;Raubal & Winter, 2002). In general, current landmark detection methodologies operate by: (a) establishing a neighborhood around a street intersection, (b) identifying the building that is an outlier relative to the other buildings in the neighborhood by analyzing a set of building attributes (e.g., width, height, color, etc.…”

Section: Introductionmentioning

confidence: 99%

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The New Generation of Automatic Landmark Detection Systems: Challenges and Guidelines

Sadeghian

Kantardzic

2008

Spatial Cognition & Computation

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Landmarks are salient objects in an environment. They play an important role in navigation by serving as orientation aids and marking decision points. Recently, there have been several efforts to design methods to automatically designate certain buildings with salient features as landmarks. All of these methodologies consist of similar steps: (a) establishing a neighborhood, usually around an intersection, (b) performing statistical or data mining analysis to find the building with outlier characteristics, and (c) establishing this salient building as the local landmark. Although these advances are significant, we believe that there are still several key issues that need to be fully addressed in order to realize the new generation of Automatic Landmark Detection Systems (ALDSs). Currently, the main shortcomings in the domain of ALDSs is the lack of a thorough and systematic study of attributes of objects that are analyzed to select landmarks, and deficient experimental verification of the benefits of ALDSs to the end users. Unless, these shortcomings are thoroughly addressed, the viability, applicability, and usefulness of ALDSs are uncertain. On the other hand, automatic landmark detection has the potential to be a dynamic, fascinating, and interdisciplinary research topic with wide applicability. Therefore, the goal of this paper is to discuss the current shortcomings in the domain of landmark detection, propose some preliminary solutions, and provide general guidelines for implementation of the new generation of ALDSs. Specifically, we discuss and promote the importance of: (a) widening the types of attributes analyzed in the landmark detection process, (b) weighting each attribute relative to its significance, (c) extending the types of objects considered as landmark candidates beyond just buildings, (d) identifying landmarks outside the vicinity of intersections, (e) identifying false landmarks along routes, and (f) using virtual environments for experiments with ALDSs. Throughout the paper, we discuss several demonstrative examples and experiments to clarify and support the ideas and concepts that are being promoted.

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Landmarks in wayfinding: a review of the existing literature

2021

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Landmarks are accepted as one of the vital elements in both virtual and real environments during wayfinding tasks. This paper provides an overview of the existing literature on the selection of landmarks in wayfinding mostly in large-scale urban environments and outdoors by discussing two main aspects of landmarks: visibility and salience. Environments and layouts used in previous studies, different tasks given to people and the main findings are explained and compared. Summary tables are created from these findings. The review concludes that there is mostly a consensus on the selection of landmarks, when considering their location. Accordingly, landmarks on route and also at decision points (with a turn) are more effective during wayfinding tasks. However, visibility of landmarks as well as visual and cognitive saliency need to be further investigated using different environments, tasks or different levels of familiarity with environments.

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Automatic Landmark Identification in Large Virtual Environment: A Spatial Data Mining Approach

Cited by 9 publications

References 9 publications

Towards a Landmark-Based Pedestrian Navigation Service Using OSM Data

Towards a Landmark-Based Pedestrian Navigation Service Using OSM Data

The New Generation of Automatic Landmark Detection Systems: Challenges and Guidelines

Landmarks in wayfinding: a review of the existing literature

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