Special Issue "Cognitive Aspects of Human-Computer Interaction for GIS"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: 28 February 2019

Special Issue Editor

Guest Editor
Prof. Dr. Dieter Fritsch

Institute for Photogrammetry, Institute of Distributed and Parallel Systems, University of Stuttgart, Stuttgart, Germany
Website | E-Mail
Phone: +49-15115286238
Interests: geographical information systems; data structures; data quality; metadata; OpenGIS; 3D GIS; Mobile GIS; GIS data Visualization, Computer Games

Special Issue Information

Dear Colleagues,

The widespread use of emerging technologies for 3D modelling and 3D visualization, such as Augmented Reality, Virtual Reality, and 3D/4D App developments, offers GIS new interfaces for Human–Computer Interactions. This also goes along with progress in big data analyses using machine learning and deep learning methods, but this is still in its infancy with regard to GIS data analysis. Most of the high-quality urban scenes, such as 3D vectorized buildings and city models, are output by interactive workflows, which should be replaced, step-by-step, by more automation in near future. Therefore, this Special Issue will deliver the state-of-the-art in 3D modeling using interactive and semi-automated and fully-automated workflows, in particular when 3D urban scenes have to be interpreted and vectorized.

Today we may let tell 3D objects its own stories, in text and messages, audio, and video. This requires the definition of storyboards to present further geometries, images, and semantics. Therefore, an integration of semantic models/ontologies with geometric data and metadata is necessary—also in order to offer semantic details in coarse-to-fine modes, just to adapt it to the user level. A child in kindergarten may play with a 2D, 3D and 4D GIS app, purely for fun, school pupils might use it to learn about their home town and its history, while students [DM1] and adults might expect more complex and dense information.

GIS is no longer the only bridge for disciplines in surveying—it has become one of many fascinating fields and technologies collaborating together, as given in the following figure. This means the data collectors, data processors and data presenters should collaborate closely; for example, we may link photogrammetry and computer vision with geoinformatics and building information modeling on the one hand, and with computer graphics and serious gaming on the other hand. The boundaries of the different fields intersect and it is exciting to see the output of these intersections. Serious gaming offers platforms for advanced 3D modeling and rendering and, therefore, also plays an important role in cognitive aspects of Human–Computer Interaction.

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Figure 1. Collaboration of several scientific fields in 2D, 3D and 4D modeling and visualization.

Therefore, this issue is open for all articles dealing with the state-of-the-art in Human–Computer Interaction and new developments, integrating Mixed Realities, 3D/4D app developments and progress in automated 3D urban scene modeling.

Prof. Dr. Dieter Fritsch
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 3D and 4D App Developments
  • Augmented Reality
  • Virtual Reality
  • Storyboard Design
  • Adaptive Ontology
  • Machine Learning
  • Deep Learning

Published Papers (6 papers)

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Research

Open AccessArticle Evaluation of User Performance in Interactive and Static 3D Maps
ISPRS Int. J. Geo-Inf. 2018, 7(11), 415; https://doi.org/10.3390/ijgi7110415
Received: 4 September 2018 / Revised: 19 October 2018 / Accepted: 23 October 2018 / Published: 26 October 2018
PDF Full-text (1997 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Interactive 3D visualizations of geospatial data are currently available and popular through various applications such as Google EarthTM and others. Several studies have focused on user performance with 3D maps, but static 3D maps were mostly used as stimuli. The main objective
[...] Read more.
Interactive 3D visualizations of geospatial data are currently available and popular through various applications such as Google EarthTM and others. Several studies have focused on user performance with 3D maps, but static 3D maps were mostly used as stimuli. The main objective of this paper was to identify differences between interactive and static 3D maps. We also explored the role of different tasks and inter-individual differences of map users. In the experimental study, we analyzed effectiveness, efficiency, and subjective preferences, when working with static and interactive 3D maps. The study included 76 participants and used a within-subjects design. Experimental testing was performed using our own testing tool 3DmoveR 2.0, which was based on a user logging method and open web technologies. We demonstrated statistically significant differences between interactive and static 3D maps in effectiveness, efficiency, and subjective preferences. Interactivity influenced the results mainly in ‘spatial understanding’ and ‘combined’ tasks. From the identified differences, we concluded that the results of the user studies with static 3D maps as stimuli could not be transferred to interactive 3D visualizations or virtual reality. Full article
(This article belongs to the Special Issue Cognitive Aspects of Human-Computer Interaction for GIS)
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Open AccessCommunication Determining Optimal Video Length for the Estimation of Building Height through Radial Displacement Measurement from Space
ISPRS Int. J. Geo-Inf. 2018, 7(9), 380; https://doi.org/10.3390/ijgi7090380
Received: 14 August 2018 / Revised: 12 September 2018 / Accepted: 14 September 2018 / Published: 18 September 2018
Cited by 1 | PDF Full-text (2198 KB) | HTML Full-text | XML Full-text
Abstract
We presented a methodology for estimating building heights in downtown Vancouver, British Columbia, Canada, using a high definition video (HDV) recorded from the International Space Station. We developed an iterative routine based on multiresolution image segmentation to track the radial displacement of building
[...] Read more.
We presented a methodology for estimating building heights in downtown Vancouver, British Columbia, Canada, using a high definition video (HDV) recorded from the International Space Station. We developed an iterative routine based on multiresolution image segmentation to track the radial displacement of building roofs over the course of the HDV, and to predict the building heights using an ordinary least-squares regression model. The linear relationship between the length of the tracking vector and the height of the buildings was excellent (r2 ≤ 0.89, RMSE ≤ 8.85 m, p < 0.01). Notably, the accuracy of the height estimates was not improved considerably beyond 10 s of outline tracking, revealing an optimal video length for estimating the height or elevation of terrestrial features. HDVs are demonstrated to be a viable and effective data source for target tracking and building height prediction when high resolution imagery, spectral information, and/or topographic data from other sources are not available. Full article
(This article belongs to the Special Issue Cognitive Aspects of Human-Computer Interaction for GIS)
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Graphical abstract

Open AccessArticle 4D Time Density of Trajectories: Discovering Spatiotemporal Patterns in Movement Data
ISPRS Int. J. Geo-Inf. 2018, 7(6), 212; https://doi.org/10.3390/ijgi7060212
Received: 10 April 2018 / Revised: 23 May 2018 / Accepted: 27 May 2018 / Published: 4 June 2018
PDF Full-text (4311 KB) | HTML Full-text | XML Full-text
Abstract
Modern positioning and sensor technology enable the acquisition of movement positions and attributes on an unprecedented scale. Therefore, a large amount of trajectory data can be used to analyze various movement phenomena. In cartography, a common way to visualize and explore trajectory data
[...] Read more.
Modern positioning and sensor technology enable the acquisition of movement positions and attributes on an unprecedented scale. Therefore, a large amount of trajectory data can be used to analyze various movement phenomena. In cartography, a common way to visualize and explore trajectory data is to use the 3D cube (e.g., space-time cube), where trajectories are presented as a tilted 3D polyline. As larger movement datasets become available, this type of display can easily become confusing and illegible. In addition, movement datasets are often unprecedentedly massive, high-dimensional, and complex (e.g., implicit spatial and temporal relations and interactions), making it challenging to explore and analyze the spatiotemporal movement patterns in space. In this paper, we propose 4D time density as a visualization method for identifying and analyzing spatiotemporal movement patterns in large trajectory datasets. The movement range of the objects is regarded as a 3D geographical space, into which the fourth dimension, 4D time density, is incorporated. The 4D time density is derived by modeling the movement path and velocity separately. We present a time density algorithm, and demonstrate it on the simulated trajectory and a real dataset representing the movement data of aircrafts in the Hong Kong International and the Macau International Airports. Finally, we consider wider applications and further developments of time density. Full article
(This article belongs to the Special Issue Cognitive Aspects of Human-Computer Interaction for GIS)
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Open AccessArticle 2DPR-Tree: Two-Dimensional Priority R-Tree Algorithm for Spatial Partitioning in SpatialHadoop
ISPRS Int. J. Geo-Inf. 2018, 7(5), 179; https://doi.org/10.3390/ijgi7050179
Received: 23 March 2018 / Revised: 1 May 2018 / Accepted: 7 May 2018 / Published: 9 May 2018
PDF Full-text (3936 KB) | HTML Full-text | XML Full-text
Abstract
Among spatial information applications, SpatialHadoop is one of the most important systems for researchers. Broad analyses prove that SpatialHadoop outperforms the traditional Hadoop in managing distinctive spatial information operations. This paper presents a Two Dimensional Priority R-Tree (2DPR-Tree) as a new partitioning technique
[...] Read more.
Among spatial information applications, SpatialHadoop is one of the most important systems for researchers. Broad analyses prove that SpatialHadoop outperforms the traditional Hadoop in managing distinctive spatial information operations. This paper presents a Two Dimensional Priority R-Tree (2DPR-Tree) as a new partitioning technique in SpatialHadoop. The 2DPR-Tree employs a top-down approach that effectively reduces the number of partitions accessed to answer the query, which in turn improves the query performance. The results were evaluated in different scenarios using synthetic and real datasets. This paper aims to study the quality of the generated index and the spatial query performance. Compared to other state-of-the-art methods, the proposed 2DPR-Tree improves the quality of the generated index and the query execution time. Full article
(This article belongs to the Special Issue Cognitive Aspects of Human-Computer Interaction for GIS)
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Open AccessArticle Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments
ISPRS Int. J. Geo-Inf. 2018, 7(5), 168; https://doi.org/10.3390/ijgi7050168
Received: 5 April 2018 / Revised: 27 April 2018 / Accepted: 30 April 2018 / Published: 2 May 2018
Cited by 2 | PDF Full-text (3215 KB) | HTML Full-text | XML Full-text
Abstract
The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of
[...] Read more.
The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008–2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1) the non-linear nature of the prediction assignment task; (2) input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3) the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS) ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R2 values, sales ratios, mean average percentage error (MAPE), coefficient of dispersion (COD)) revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction. Full article
(This article belongs to the Special Issue Cognitive Aspects of Human-Computer Interaction for GIS)
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Open AccessArticle An Efficient Visualization Method for Polygonal Data with Dynamic Simplification
ISPRS Int. J. Geo-Inf. 2018, 7(4), 138; https://doi.org/10.3390/ijgi7040138
Received: 12 February 2018 / Revised: 14 March 2018 / Accepted: 21 March 2018 / Published: 2 April 2018
PDF Full-text (17028 KB) | HTML Full-text | XML Full-text
Abstract
Polygonal data often require rendering with symbolization and simplification in geovisualization. A common issue in existing methods is that simplification, symbolization and rendering are addressed separately, causing computational and data redundancies that reduce efficiency, especially when handling large complex polygonal data. Here, we
[...] Read more.
Polygonal data often require rendering with symbolization and simplification in geovisualization. A common issue in existing methods is that simplification, symbolization and rendering are addressed separately, causing computational and data redundancies that reduce efficiency, especially when handling large complex polygonal data. Here, we present an efficient polygonal data visualization method by organizing the simplification, tessellation and rendering operations into a single mesh generalization process. First, based on the sweep line method, we propose a topology embedded trapezoidal mesh data structure to organize the tessellated polygons. Second, we introduce horizontal and vertical generalization operations to simplify the trapezoidal meshes. Finally, we define a heuristic testing algorithm to efficiently preserve the topological consistency. The method is tested using three OpenStreetMap datasets and compared with the Douglas Peucker algorithm and the Binary Line Generalization tree-based method. The results show that the proposed method improves the rendering efficiency by a factor of six. Efficiency-sensitive mapping applications such as emergency mapping could benefit from this method, which would significantly improve their visualization performances. Full article
(This article belongs to the Special Issue Cognitive Aspects of Human-Computer Interaction for GIS)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Determining Optimal Video Length for the Estimation of Building Height through Radial Displacement Measurement from Space

Abstract: We present a methodology for estimating building heights in downtown Vancouver, British Columbia (Canada) using a high definition video (HDV) recorded from the International Space Station. We developed an iterative routine based on multiresolution image segmentation to track the radial displacement of building roofs over the course of the HDV and predict the building heights using an ordinary least-squares regression model. The linear relationship between the length of the tracking vector and the height of the buildings was excellent (r2 ≤ 0.89, RMSE ≤ 8.85 m, p < 0.01). Notably, the accuracy of the height estimates was not improved considerably beyond 10 seconds of outline tracking, revealing an optimal video length for estimating the height or elevation of terrestrial features. HDVs are demonstrated to be a viable and effective data source for target tracking and building height prediction when high resolution imagery, spectral information and/or topographic data from other sources is not available.

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