1,309 research outputs found
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>
Wayfinding Stages: The Role of Familiarity, Gaze Events, and Visual Attention
Full text linkUnderstanding the cognitive processes involved in wayfinding is crucial for both theoretical advances and practical applications in navigation systems development. This study explores how gaze behavior and visual attention contribute to our understanding of cognitive states during wayfinding. Based on the model proposed by Downs and Stea, which segments wayfinding into four distinct stages: self-localization, route planning, monitoring, and goal recognition, we conducted an outdoor wayfinding experiment with 56 participants. Given the significant role of spatial familiarity in wayfinding behavior, each participant navigated six different routes in both familiar and unfamiliar environments, with their eye movements being recorded. We provide a detailed examination of participants' gaze behavior and the actual objects of focus. Our findings reveal distinct gaze behavior patterns and visual attention, differentiating wayfinding stages while emphasizing the impact of spatial familiarity. This examination of visual engagement during wayfinding explains adaptive cognitive processes, demonstrating how familiarity influences navigation strategies. The results enhance our theoretical understanding of wayfinding and offer practical insights for developing navigation aids capable of predicting different wayfinding stages
Rethinking Route Choices! On the Importance of Route Selection in Wayfinding Experiments
Full text linkRoute 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
I Can Tell by Your Eyes! Continuous Gaze-Based Turn-Activity Prediction Reveals Spatial Familiarity
Full text linkSpatial 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
The Impact of Traffic Lights on Modal Split and Route Choice: A use-case in Vienna
No full text<p>The data and code scripts used for the analysis in the paper entitled "<strong>The Impact of Traffic Lights on Modal Split and Route Choice: A use-case in Vienna</strong>", submitted to AGILE (Association of Geographic Information Laboratories in Europe) 2024 Conference.</p><p>It comprises three folders within the zip file:</p><ol><li><strong>Data</strong>: Contains the datasets for the analysis.</li><li><strong>Code</strong>: Includes script files essential for conducting the analysis. The scripts are written in Python.</li><li><strong>Results</strong>: Includes the outcomes showcased in the associated paper.</li><li><strong>Visualizations</strong> : Includes a jupyter notebook for the generated plots in the the associated paper.</li></ol><p>Programming Language: Python </p><p>For reproducibility read the README.txt file included in the zip folder.</p><p>All data files are licensed under CC BY 4.0, all software is licensed under MIT License.</p><p>The transportation dynamics within a European city, Vienna, are examined using a multi-graph representation of the city's network. The focus is on time-optimized routing algorithms and the effects of altering the average waiting penalty at traffic lights. The impact of these modifications, whether an increase to 60, 90, or even 150 seconds or a decrease to 10 seconds, is observed in the selection of transportation modes and routes for identical origin and destination pairs. The investigation also extends to whether routes shift towards secondary street networks to avoid traffic lights as the waiting penalty increases. Experimental variations in average waiting time for cars aim to uncover detailed effects on transportation mode choices, route length and time changes, and variations in human energy expenditure. These findings could provide valuable insights into the transportation network and its possibilities and help in urban planning and policy development. The results indicate a shift in transportation mode as the waiting penalty for cars at traffic lights increases, and in some instances, routes are redirected to roads of lower importance such as residential or service roads.</p>
Attention as an input modality for Post-WIMP interfaces using the viGaze eye tracking framework
No full textEye tracking is one of the most prominent modalities to track user attention while interacting with computational devices. Today, most of the current eye tracking frameworks focus on tracking the user gaze during website browsing or while performing other tasks and interactions with a digital device. Most frameworks have in common that they do not exploit gaze as an input modality. In this paper we describe the realization of a framework
named viGaze. Its main goal is to provide an easy to use framework to exploit the use of eye gaze as an input modality in various contexts. Therefore it provides features to explore explicit and implicit interactions in complex virtual environments by using the eye gaze of
a user for various interactions. The viGaze framework is flexible and can be easily extended to incorporate other input modalities typically used in Post-WIMP interfaces such as gesture or foot input. In this paper we describe the key components of our viGaze framework and
additionally describe a user study that was conducted to test the framework. The user study took place in a virtual retail environment, which provides a challenging pervasive environment and contains complex interactions that can be supported by gaze. The participants performed two gaze-based interactions with products on virtual shelves and started an interaction cycle between the products and an advertisement monitor placed on the shelf. We demonstrate how gaze can be used in Post-WIMP interfaces to steer the attention of users to certain components of the system. We conclude by discussing the advantages provided through the viGaze framework and highlighting the potentials of gaze-based interaction
Geospatial Semantics for Spatial Prediction (Short Paper)
No full textIn this paper the potential of geospatial semantics for spatial predictions is explored. Therefore data from the LinkedGeoData platform is used to predict landcover classes described by the CORINE dataset. Geo-objects obtained from LinkedGeoData are described by an OWL ontology, which is utilized for the purpose of spatial prediction within this paper. This prediction is based on an association analysis which computes the collocations between the landcover classes and the semantically described geo-objects. The paper provides an analysis of the learned association rules and finally concludes with a discussion on the promising potential of geospatial semantics for spatial predictions, as well as potentially fruitful future research within this domain
sj-docx-1-jet-10.1177_15266028231157642 – Supplemental material for Geniculate Artery Endovascular Embolization Post-Total Knee Arthroplasty for Hemarthrosis Treatment: A Systematic Review of the Literature
No full textSupplemental material, sj-docx-1-jet-10.1177_15266028231157642 for Geniculate Artery Endovascular Embolization Post-Total Knee Arthroplasty for Hemarthrosis Treatment: A Systematic Review of the Literature by Christina M. Melian, Stefanos Giannopoulos, Ioannis Tsouknidas, Panagiotis Volteas, Dimitrios Virvilis and George J. Koullias in Journal of Endovascular Therapy</p
Navigating Your Way! Increasing the Freedom of Choice During Wayfinding
Full text linkUsing 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
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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