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

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 41585

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Special Issue Editors

Dr. Éric Guilbert

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Guest Editor

Department of Geomatics Sciences, Université Laval, Québec, QC G1V 0A6, Canada
Interests: geometrical modelling; terrain modelling; cartographic generalisation

Dr. Paweł Bogusławski

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Guest Editor

Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wroclaw, Poland
Interests: topological data structures; spatial modelling; BIM-GIS integration

Prof. Dr. Ümit Işikdağ

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Guest Editor

Department of Informatics, Mimar Sinan Fine Arts University, Istanbul 34427, Turkey
Interests: BIM; 3D GIS; internet of things; machine learning; deep learning
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Special Issue Information

Dear Colleagues,

People want to observe and understand the world around them. They want to make quick and sound decisions at their level of management: A building, factory, city, or perhaps a whole country. It is not an easy task, especially in a rapidly-changing environment where more and more information from different domains have to be taken into account. Old good models and analysis techniques have become outdated, unable to cope with the level of complexity. People need new and advanced tools, GISystems, to analyse, understand and manage their resources.

The advent of new data collection technologies, such as LiDAR and drones, have made geospatial data available in large amounts and at low costs. While access to data is getting easier, their increasing size imposes that manual interventions are limited as much as possible. This means that geospatial tools have to evolve towards further automation and guarantee the reproducibility of the process and the quality of the results. As such, algorithms and data structures for handling geospatial data also need to be more and more robust and efficient to model complex, multidimensional geospatial phenomena in GISystems and provide higher levels of analysis.

This Special Issue focuses on the multidimensional modelling and representation of geospatial data. This includes spatial dimensions but also temporal and scale dimensions. We invite papers with original contributions, proposing new algorithms and data structures to handle geospatial data and their representation at multiple scale. Papers presented in the ISPRS Commission IV Symposium in Delft, October 2018 will be considered if they are extended to full journal papers. Topics of this Special Issue mainly focus on, but are not limited to:

Geographic and spatial information systems
Algorithms and data structures for modelling spatial phenomena
Multiple representation of geospatial phenomena
Topological data structures for multidimensional geometries
Data structures for multiscale representations
Algorithms for handling multiple representations
Algorithms and data structures for terrain modelling
Machine learning utilizing multidimensional and multiscale models
Application of multidimensional models to city/urban information systems, BIM-GIS integration, infrastructure monitoring, disaster management, natural resources (forestry, mining, geology, etc.)

Dr. Éric Guilbert
Dr. Paweł Bogusławski
Dr. Umit Isikdag
Guest Editors

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 submissions that pass pre-check are 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 1700 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.

Published Papers (10 papers)

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Editorial

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3 pages, 193 KiB

Open AccessEditorial

Multidimensional and Multiscale GIS

by Eric Guilbert, Pawel Boguslawski and Umit Isikdag

ISPRS Int. J. Geo-Inf. 2019, 8(12), 523; https://doi.org/10.3390/ijgi8120523 - 23 Nov 2019

Cited by 1 | Viewed by 1872

Abstract

The advent of new data collection technologies, such as LiDAR and drones, have made geospatial data available in large amounts and at low costs. While access to data is getting easier, geospatial tools have to evolve towards further automation and guarantee the reproducibility of the process and the quality of the results. As such, algorithms and data structures for handling geospatial data also need to be more and more robust and efficient to model complex, multidimensional geospatial phenomena in GISystems and provide higher levels of analysis. Articles in this special issue address two complementary aspects of the problem. They introduce new algorithms and data structures that allow for a more efficient handling of multidimensional data but also present complete processing chains dealing with the integration and the dissemination of multidimensional data. Full article

(This article belongs to the Special Issue Multidimensional and Multiscale GIS)

Research

Jump to: Editorial

17 pages, 975 KiB

Open AccessArticle

Incorporating Topological Representation in 3D City Models

by Stelios Vitalis, Ken Arroyo Ohori and Jantien Stoter

ISPRS Int. J. Geo-Inf. 2019, 8(8), 347; https://doi.org/10.3390/ijgi8080347 - 01 Aug 2019

Cited by 14 | Viewed by 3990

Abstract

3D city models are being extensively used in applications such as evacuation scenarios and energy consumption estimation. The main standard for 3D city models is the CityGML data model which can be encoded through the CityJSON data format. CityGML and CityJSON use polygonal modelling in order to represent geometries. True topological data structures have proven to be more computationally efficient for geometric analysis compared to polygonal modelling. In a previous study, we have introduced a method to topologically reconstruct CityGML models while maintaining the semantic information of the dataset, based solely on the combinatorial map (C-Map) data structure. As a result of the limitations of C-Map’s semantic representation mechanism, the resulting datasets could suffer either from semantic information loss or the redundant repetition of them. In this article, we propose a solution for a more efficient representation of geometry, topology and semantics by incorporating the C-Map data structure into the CityGML data model and implementing a CityJSON extension to encode the C-Map data. In addition, we provide an algorithm for the topological reconstruction of CityJSON datasets to append them according to this extension. Finally, we apply our methodology to three open datasets in order to validate our approach when applied to real-world data. Our results show that the proposed CityJSON extension can represent all geometric information of a city model in a lossless way, providing additional topological information for the objects of the model. Full article

(This article belongs to the Special Issue Multidimensional and Multiscale GIS)

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21 pages, 953 KiB

Open AccessArticle

Development of an Indoor Space Semantic Model and Its Implementation as an IndoorGML Extension

by Nishith Maheshwari, Srishti Srivastava and Krishnan Sundara Rajan

ISPRS Int. J. Geo-Inf. 2019, 8(8), 333; https://doi.org/10.3390/ijgi8080333 - 27 Jul 2019

Cited by 9 | Viewed by 3945

Abstract

Geospatial data capture and handling of indoor spaces is increasing over the years and has had a varied history of data sources ranging from architectural and building drawings to indoor data acquisition approaches. While these have been more data format and information driven primarily for the physical representation of spaces, it is important to note that many applications look for the semantic information to be made available. This paper proposes a space classification model leading to an ontology for indoor spaces that accounts for both the semantic and geometric characteristics of the spaces. Further, a Space semantic model is defined, based on this ontology, which can then be used appropriately in multiple applications. To demonstrate the utility of the model, we also present an extension to the IndoorGML data standard with a set of proposed classes that can help capture both the syntactic and semantic components of the model. It is expected that these proposed classes can be appropriately harnessed for use in diverse applications ranging from indoor data visualization to more user customised building evacuation path planning with a semantic overtone. Full article

(This article belongs to the Special Issue Multidimensional and Multiscale GIS)

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22 pages, 10438 KiB

Open AccessArticle

Evaluation of Topological Consistency in CityGML

by Anna Giovanella, Patrick Erik Bradley and Sven Wursthorn

ISPRS Int. J. Geo-Inf. 2019, 8(6), 278; https://doi.org/10.3390/ijgi8060278 - 14 Jun 2019

Cited by 9 | Viewed by 3792

Abstract

Boundary representation models are data models that represent the topology of a building or city model. This leads to an issue in combination with geometry, as the geometric model necessarily has an underlying topology. In order to allow topological queries to rely on the incidence graph only, a new notion of topological consistency is introduced that captures possible topological differences between the incidence graph and the topology coming from geometry. Intersection matrices then describe possible types of topological consistency and inconsistency. As an application, it is examined which matrices can occur as intersection matrices, and how matrices from topologically consistent data look. The analysis of CityGML data sets stored in a spatial database system then shows that many real-world data sets contain many topologically inconsistent pairs of polygons. It was observed that even if data satisfy the val3dity test, they can still be topologically inconsistent. On the other hand, it is shown that the ISO 19107 standard is equivalent to our notion of topological consistency. In the case when the intersection is a point, topological inconsistency occurs because a vertex lies on a line segment. However, the most frequent topological inconsistencies seem to arise when the intersection of two polygons is a line segment. Consequently, topological queries in present CityGML data cannot rely on the incidence graph only, but must always make costly geometric computations if correct results are to be expected. Full article

(This article belongs to the Special Issue Multidimensional and Multiscale GIS)

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20 pages, 2419 KiB

Open AccessArticle

Learning Cartographic Building Generalization with Deep Convolutional Neural Networks

by Yu Feng, Frank Thiemann and Monika Sester

ISPRS Int. J. Geo-Inf. 2019, 8(6), 258; https://doi.org/10.3390/ijgi8060258 - 30 May 2019

Cited by 70 | Viewed by 6950

Abstract

Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g., simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the human operator is the benchmark, who is able to design an aesthetic and correct representation of the physical reality. Deep learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform traditional computer vision methods. In both domains-computer vision and cartography-humans are able to produce good solutions. A prerequisite for the application of deep learning is the availability of many representative training examples for the situation to be learned. As this is given in cartography (there are many existing map series), the idea in this paper is to employ deep convolutional neural networks (DCNNs) for cartographic generalizations tasks, especially for the task of building generalization. Three network architectures, namely U-net, residual U-net and generative adversarial network (GAN), are evaluated both quantitatively and qualitatively in this paper. They are compared based on their performance on this task at target map scales 1:10,000, 1:15,000 and 1:25,000, respectively. The results indicate that deep learning models can successfully learn cartographic generalization operations in one single model in an implicit way. The residual U-net outperforms the others and achieved the best generalization performance. Full article

(This article belongs to the Special Issue Multidimensional and Multiscale GIS)

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19 pages, 12898 KiB

Open AccessArticle

Multidimensional Web GIS Approach for Citizen Participation on Urban Evolution

by Frederick Lafrance, Sylvie Daniel and Suzana Dragićević

ISPRS Int. J. Geo-Inf. 2019, 8(6), 253; https://doi.org/10.3390/ijgi8060253 - 30 May 2019

Cited by 18 | Viewed by 4970

Abstract

Web-mapping has been widely used to facilitate citizen participation in smart cities. Web-mapping has evolved from 2D static maps towards more dynamic and immersive 3D worlds such as virtual globes and scenes. Although current technologies allow us to build multidimensional representations, there is still a lack of research studies on how to further leverage them to foster citizen participation. Information on space–time changes can be an important asset for a successful citizen participation process. Citizens may need to track the evolution of their city over space and time, and how their participation will impact the urban planning and decision-making process. Consequently, the main objective of this research study is to design and develop a multidimensional (2D, 3D, 4D) web-mapping platform where citizens can better assess and understand the spatiotemporal evolution of their cities. User testing was conducted to assess the multidimensional representation of the animations used, and the spatiotemporal mechanism and interface features. Results showed that it is recommended to integrate spatiotemporal simulations to citizen participation platforms so citizens can better assess the impacts of their choices. We also assessed that 3D does not always communicate information better than 2D. Future work will aim at testing the platform in a consultation process with a representative sample of participants. Full article

(This article belongs to the Special Issue Multidimensional and Multiscale GIS)

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18 pages, 9463 KiB

Open AccessArticle

Obstacle-Aware Indoor Pathfinding Using Point Clouds

by Lucía Díaz-Vilariño, Pawel Boguslawski, Kourosh Khoshelham and Henrique Lorenzo

ISPRS Int. J. Geo-Inf. 2019, 8(5), 233; https://doi.org/10.3390/ijgi8050233 - 19 May 2019

Cited by 15 | Viewed by 4690

Abstract

With the rise of urban population, updated spatial information of indoor environments is needed in a growing number of applications. Navigational assistance for disabled or aged people, guidance for robots, augmented reality for gaming, and tourism or training emergency assistance units are just a few examples of the emerging applications requiring real three-dimensional (3D) spatial data of indoor scenes. This work proposes the use of point clouds for obstacle-aware indoor pathfinding. Point clouds are firstly used for reconstructing semantically rich 3D models of building structural elements in order to extract initial navigational information. Potential obstacles to navigation are classified in the point cloud and directly used to correct the path according to the mobility skills of different users. The methodology is tested in several real case studies for wheelchair and ordinary users. Experiments show that, after several iterations, paths are readapted to avoid obstacles. Full article

(This article belongs to the Special Issue Multidimensional and Multiscale GIS)

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11 pages, 9933 KiB

Open AccessArticle

Terrain Representation and Distinguishing Ability of Roughness Algorithms Based on DEM with Different Resolutions

by Jiang Wu, Junjie Fang and Jiangbo Tian

ISPRS Int. J. Geo-Inf. 2019, 8(4), 180; https://doi.org/10.3390/ijgi8040180 - 06 Apr 2019

Cited by 6 | Viewed by 3106

Abstract

Digital elevation model (DEM) resolution is closely related to the degree of expression of real terrain, the extraction of terrain parameters, and the uncertainty of statistical models. Therefore, based on DEMs with various resolutions, this paper explores the representation and distinguishing ability of different roughness algorithms to measure terrain parameters. Fuyang, a district of Hangzhou City with various landform types, was selected as the research area. Slope, root mean squared height, vector deviation, and two-dimensional continuous wavelet transform were selected as four typical roughness algorithms. The resolutions used were 5, 10, 25, and 50 m DEM on the scale for plains, hills, and mountainous areas. The statistical criteria of effect size and entropy were used as indicators to evaluate and analyze the different roughness algorithms. The results show that in terms of these measures: (1) The expression ability of the SLOPE and root mean squared height (RMSH) algorithms is better than that of the vector deviation method, while the two-dimensional continuous wavelet method based on frequency analysis emphasizes the terrain information within a certain range. (2) The terrain distinguishing ability of the SLOPE and RMSH is not sensitive to the changes in resolution, with the other two algorithms varying with the changes in resolution. Full article

(This article belongs to the Special Issue Multidimensional and Multiscale GIS)

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17 pages, 6894 KiB

Open AccessArticle

Integration, Processing and Dissemination of LiDAR Data in a 3D Web-GIS

by Marek Kulawiak, Marcin Kulawiak and Zbigniew Lubniewski

ISPRS Int. J. Geo-Inf. 2019, 8(3), 144; https://doi.org/10.3390/ijgi8030144 - 19 Mar 2019

Cited by 16 | Viewed by 3884

Abstract

The rapid increase in applications of Light Detection and Ranging (LiDAR) scanners, followed by the development of various methods that are dedicated for survey data processing, visualization, and dissemination constituted the need of new open standards for storage and online distribution of collected three-dimensional data. However, over a decade of research in the area has resulted in a number of incompatible solutions that offer their own ways of disseminating results of LiDAR surveys (be it point clouds or reconstructed three-dimensional (3D) models) over the web. The article presents a unified system for remote processing, storage, visualization, and dissemination of 3D LiDAR survey data, including 3D model reconstruction. It is built with the use of open source technologies and employs open standards, such as 3D Tiles, LASer (LAS), and Object (OBJ) for data distribution. The system has been deployed for automatic organization, processing, and dissemination of LiDAR surveys that were performed in the city of Gdansk. The performance of the system has been measured using a selection of LiDAR datasets of various sizes. The system has shown to considerably simplify the process of data organization and integration, while also delivering tools for easy discovery, inspection, and acquisition of desired datasets. Full article

(This article belongs to the Special Issue Multidimensional and Multiscale GIS)

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13 pages, 6605 KiB

Open AccessArticle

A Modified Methodology for Generating Indoor Navigation Models

by Elżbieta Lewandowicz, Przemysław Lisowski and Paweł Flisek

ISPRS Int. J. Geo-Inf. 2019, 8(2), 60; https://doi.org/10.3390/ijgi8020060 - 29 Jan 2019

Cited by 17 | Viewed by 3508

Abstract

Automatic methods for constructing navigation routes do not fully meet all requirements. The aim of this study was to modify the methodology for generating indoor navigation models based on the Medial Axis Transformation (MAT) algorithm. The simplified method for generating corridor axes relies on the Node-Relation Structure (NRS) methodology. The axis of the modeled structure (corridor) is then determined based on the points of the middle lines intersecting the structure (polygon). The proposed solution involves a modified approach to the segmentation of corridor space. Traditional approaches rely on algorithms for generating Triangulated Irregular Networks (TINs) by Delaunay triangulation or algorithms for generating Thiessen polygons known as Voronoi diagrams (VDs). In this study, both algorithms were used in the segmentation process. The edges of TINs intersected structures. Selected midpoints on TIN edges, which were located in the central part of the structure, were used to generate VDs. Corridor structures were segmented by polygon VDs. The identifiers or structure nodes were the midpoints on the TIN edges rather than the calculated centroids. The generated routes were not zigzag lines, and they approximated natural paths. The main advantage of the proposed solution is its simplicity, which can be attributed to the use of standard tools for processing spatial data in a geographic information system. Full article

(This article belongs to the Special Issue Multidimensional and Multiscale GIS)

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