2,635 research outputs found
How Subdimensions of Salience Influence Each Other. Comparing Models Based on Empirical Data
Get PDFTheories about salience of landmarks in GIScience have been evolving for about 15 years. This paper empirically analyses hypotheses about the way different subdimensions (visual, structural, and cognitive aspects, as well as prototypicality and visibility in advance) of salience have an impact on each other. The analysis is based on empirical data acquired by means of an in-situ survey (360 objects, 112 participants). It consists of two parts: First, a theory-based structural model is assessed using variance-based Structural Equation Modeling. The results achieved are, second, corroborated by a data-driven approach, i.e. a tree-augmented naive Bayesian network is learned. This network is used as a structural model input for further analyses. The results clearly indicate that the subdimensions of salience influence each other
GeoAR A calibration method for Geographic-aware augmented reality: Getting started
No full text<h2>How to cite</h2>
<p>Please, don't forget to cite the original research article that result in this application:</p>
<p>Galvão, M. L., Fogliaroni, P., Giannopoulos, I., Navratil, G., Kattenbeck, M., & Alinaghi, N. (2024). <a href="https://www.tandfonline.com/doi/full/10.1080/13658816.2024.2355326"><strong>GeoAR: a calibration method for Geographic-Aware Augmented Reality</strong></a>. <em>International Journal of Geographical Information Science</em>, 1–27. <a href="https://doi.org/10.1080/13658816.2024.2355326">https://doi.org/10.1080/13658816.2024.2355326</a></p>
<h2>GeoAR getting started application</h2>
<p>This getting started tutorial provides the basic information so you can implement your own geographic-aware AR application.</p>
<p>The project we provide here is described in the IJGIS article GeoAR: A calibration method for Geographic-aware augmented reality, and it provides the means for all four calibration approaches described in the article.</p>
<p>The set-up we provide here is for the device Microsoft Hololens 2, but feel free to adpat the code to use in different devices.</p>
<h2><strong>Basic requirements</strong></h2>
<p>In order to run and develop your GeoAR application using this project it is required the following:</p>
<ul>
<li>AR device (Microsoft Hololens 2)</li>
<li>Unity Hub with Unity 2021.3.2f1 installed (adaptations for a later version of Unity might be necessary)</li>
<li>Microsoft Visual Studio (Version 16.11.15 or later)</li>
<li>Mixed Reality Toolkit (MRTK) foundation package for Unity (2.8.0.0)</li>
</ul>
<p>If you do not have experience in developing with Unity or MRTK, we highly recommend you go through the following Microsoft training modules:</p>
<p><a href="https://learn.microsoft.com/en-us/training/modules/learn-mrtk-tutorials/1-1-introduction">Introduction to the Mixed Reality Toolkit – Set Up Your Project and Use Hand Interaction</a></p>
<p><a href="https://learn.microsoft.com/en-us/training/modules/intro-to-mixed-reality/">Introduction to mixed reality</a></p>
<h2><strong>1. Download and </strong>open<strong> </strong>the <strong>project in Unity</strong></h2>
<ol>
<li>Download the project folder and unpack it in your local machine</li>
<li>Use Unity Hub to open the project folder GeoARUnityProject (make sure you have the right version installed)</li>
<li>If everything is correct, you will be able to play the application in the game mode.</li>
</ol>
<p>Further instructions with video tutorials can be found here :</p>
<p><a href="https://geoinfo.geo.tuwien.ac.at/geoar-getting-started/">https://geoinfo.geo.tuwien.ac.at/geoar-getting-started/</a></p>
<h3><strong>License</strong></h3>
<p>All data is published under the CC-BY 4.0 license. The code is under the GNU General public license</p>
I Can Tell by Your Eyes! Continuous Gaze-Based Turn-Activity Prediction Reveals Spatial Familiarity
Get PDFSpatial familiarity plays an essential role in the wayfinding decision-making process. Recent findings in wayfinding activity recognition domain suggest that wayfinders' turning behavior at junctions is strongly influenced by their spatial familiarity. By continuously monitoring wayfinders' turning behavior as reflected in their eye movements during the decision-making period (i.e., immediately after an instruction is received until reaching the corresponding junction for which the instruction was given), we provide evidence that familiar and unfamiliar wayfinders can be distinguished. By applying a pre-trained XGBoost turning activity classifier on gaze data collected in a real-world wayfinding task with 33 participants, our results suggest that familiar and unfamiliar wayfinders show different onset and intensity of turning behavior. These variations are not only present between the two classes -familiar vs. unfamiliar- but also within each class. The differences in turning-behavior within each class may stem from multiple sources, including different levels of familiarity with the environment
Rethinking Route Choices! On the Importance of Route Selection in Wayfinding Experiments
Get PDFRoute selection for a wayfinding experiment is not a trivial task and is often made in an undocumented way. Only recently (2021), a systematic, reproducible and score-based approach for route selection for wayfinding experiments was published. However, it is still unclear how robust study results are across all potential routes in a particular experimental area. An important share of routes might lead to different conclusions than most routes. This share would distort and/or invert the study outcome. If so, the question of selecting routes that are unlikely to distort the results of our wayfinding experiments remains unanswered. In order to answer these questions, an agent-based simulation study with four different sample sizes (N = 15, 25, 50, 3000 agents) comparing Turn-by-Turn and Free Choice Navigation approaches (between-subject design) regarding their arrival rates on more than 11000 routes in the city center of Vienna, Austria, was run. The results of our study indicate that with decreasing sample size, there is an increase in the share of routes which lead to contradictory results regarding the arrival rate, i.e., the results become less robust. Therefore, based on simulation results, we present an approach for selecting suitable routes even for small-scale in-situ studies
Is Salience Robust? A Heterogeneity Analysis of Survey Ratings
Get PDFDiffering weights for salience subdimensions (e.g. visual or structural salience) have been suggested since the early days of salience models in GIScience. Up until now, however, it remains unclear whether weights found in studies are robust across environments, objects and observers. In this study we examine the robustness of a survey-based salience model. Based on ratings of N_{o}=720 objects by N_{p}=250 different participants collected in-situ in two different European cities (Regensburg and Augsburg) we conduct a heterogeneity analysis taking into account environment and sense of direction stratified by gender. We find, first, empirical evidence that our model is invariant across environments, i.e. the strength of the relationships between the subdimensions of salience does not differ significantly. The structural model coefficients found can, hence, be used to calculate values for overall salience across different environments. Second, we provide empirical evidence that invariance of our measurement model is partly not given with respect to both, gender and sense of direction. These compositional invariance problems are a strong indicator for personal aspects playing an important role
Navigating Your Way! Increasing the Freedom of Choice During Wayfinding
Get PDFUsing navigation assistance systems has become widespread and scholars have tried to mitigate potentially adverse effects on spatial cognition these systems may have due to the division of attention they require. In order to nudge the user to engage more with the environment, we propose a novel navigation paradigm called Free Choice Navigation balancing the number of free choices, route length and number of instructions given. We test the viability of this approach by means of an agent-based simulation for three different cities. Environmental spatial abilities and spatial confidence are the two most important modeled features of our agents. Our results are very promising: Agents could decide freely at more than 50% of all junctions. More than 90% of the agents reached their destination within an average distance of about 125% shortest path length
Front Matter, Table of Contents, Preface, Conference Organization
No full textFront Matter, Table of Contents, Preface, Conference Organizatio
Will You Take This Turn? Gaze-Based Turning Activity Recognition During Navigation
Get PDFDecision making is an integral part of wayfinding and people progressively use navigation systems to facilitate this task. The primary decision, which is also the main source of navigation error, is about the turning activity, i.e., to decide either to turn left or right or continue straight forward. The fundamental step to deal with this error, before applying any preventive approaches, e.g., providing more information, or any compensatory solutions, e.g., pre-calculating alternative routes, could be to predict and recognize the potential turning activity. This paper aims to address this step by predicting the turning decision of pedestrian wayfinders, before the actual action takes place, using primarily gaze-based features. Applying Machine Learning methods, the results of the presented experiment demonstrate an overall accuracy of 91% within three seconds before arriving at a decision point. Beyond the application perspective, our findings also shed light on the cognitive processes of decision making as reflected by the wayfinder’s gaze behaviour: incorporating environmental and user-related factors to the model, results in a noticeable change with respect to the importance of visual search features in turn activity recognition
I Can Tell by Your Eyes! Continuous Gaze-Based Turn-Activity Prediction Reveals Spatial Familiarity
No full text<p>The data used for the analysis in the paper entitled "<strong>I Can Tell by Your Eyes! Continuous Gaze-Based Turn-Activity Prediction Reveals Spatial Familiarity</strong>" published in LIPIcs, Volume 240, COSIT 2022</p>
<h2><strong>How to Cite?</strong></h2>
<p><a href="https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.2">Alinaghi, N., Kattenbeck, M., & Giannopoulos, I. (2022). I can tell by your eyes! continuous gaze-based turn-activity prediction reveals spatial familiarity. In <em>15th International Conference on Spatial Information Theory (COSIT 2022)</em>. Schloss-Dagstuhl-Leibniz Zentrum für Informatik.</a></p><p>Spatial familiarity plays an essential role in the wayfinding decision-making process. Recent findings in wayfinding activity recognition domain suggest that wayfinders' turning behavior at junctions is strongly influenced by their spatial familiarity. By continuously monitoring wayfinders' turning behavior as reflected in their eye movements during the decision-making period (i.e., immediately after an instruction is received until reaching the corresponding junction for which the instruction was given), we provide evidence that familiar and unfamiliar wayfinders can be distinguished. By applying a pre-trained XGBoost turning activity classifier on gaze data collected in a real-world wayfinding task with 33 participants, our results suggest that familiar and unfamiliar wayfinders show different onset and intensity of turning behavior. These variations are not only present between the two classes -familiar vs. unfamiliar- but also within each class. The differences in turning-behavior within each class may stem from multiple sources, including different levels of familiarity with the environment.</p>
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