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State-of-the-Art in Spatial Information Science
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State-of-the-Art in Spatial Information Science

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

Deadline for manuscript submissions: closed (1 July 2020) | Viewed by 115476

Special Issue Editors

Prof. Dr. Songnian Li

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Guest Editor
Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON, Canada
Interests: big geospatial data analytics; data mining and knowledge discovery; human mobility; spatio-temporal dynamics; citizen science; digital twins for smart cities
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sisi Zlatanova

E-Mail Website
Guest Editor
Faculty of Built Environment, University of New South Wales, Sydney, NSW 2052, Australia
Interests: 3D indoor modelling; 3D GIS; integration of BIM and GIS; 3D spatial analysis; DBMS; emergency response
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maria Antonia Brovelli

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Politecnico di Milano, P.zza Leonardo da Vinci, 32, Building 3, 20133 Milano, Italy
Interests: geospatial web; geodata science; citizen science; open science; open data; open geospatial software; geospatial artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Monika Sester

E-Mail Website
Guest Editor
Institute of Cartography and Geoinformatics, Leibniz University Hannover, Welfengarten 1, 30167 Hannover, Germany
Interests: multiscale data; data integration; trajectory mining; traffic data analysis; integrated analysis of VGI data; machine learning and deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Photogrammetry, remote sensing and the spatial information sciences have witnessed great changes over the last few years towards the 3D representation, processing and simulation of big data. The reasons for this development are, on the one hand, new societal and political challenges such as a stronger and more elaborated quest for sustainable development, a globally increasing urban population, a more apparent sense of safety and security, and an increased globalisation, visible for example in the mobility of people, goods, capital and education. On the other hand, innovation in this field has been strongly influenced by progress in information and communication technology, which can be summarized in terms of ubiquitous computing, geo-sensor networks, 3D geospatial data infrastructures, digital earth, geospatial big data, cloud computing, web 2.0, the internet of things, and crowd sourcing.

These developments have had a profound impact on the theory and development of the spatial information sciences and the operational use of spatial information, which relates to one of the three main ISPRS scientific interests. With ever increasing amounts of spatial data collected by new sensors such as 3D cameras, interferometric SAR sensors and flash lasers and through crowdsourcing, new methodologies for automatic information and knowledge extraction through data sciences (e.g., data mining, machine learning, deep learning) and ontologies, semantics and knowledge representation for geospatial information have been developed to boost a large variety of new applications such as 3D virtual environments, city analytics and simulation, urban mobility, autonomous and assisted driving, indoor/outdoor modelling, augmented virtual reality and geo-computation, as well as advanced applications in smart cites.  

This Special Issue, as part of the preparation for 2020 ISPRS Congress, will review and document recent developments in spatial information science with a focus on multi-dimensional (3D, 4D, 5D ...) representations, geospatial big spatial data processing and applications, in addition to research spanning the theoretical foundations of spatial information science, through computation with spatial information, to technologies for spatial information use. High-quality contributions are invited in the form of comprehensive scientific review papers and position papers that address the state-of-the-art in a particular branch of spatial information science. Papers targeting more recently developed fields or research directions are especially welcome.

Papers must be original contributions, not previously published or submitted to other journals. Papers published or submitted for publication in conference proceedings may be considered if they are considerably extended and improved. Authors must use the provided Microsoft Word template or LaTeX template to prepare their manuscript and must follow the instructions for authors at https://www.mdpi.com/journal/ijgi/instructions.

Please send an email to the Guest Editors ([email protected]) to let us know your interest in contributing a paper as soon as possible, preferably with a tentative title and a brief abstract.

Prof. Dr. Songnian Li
Prof. Dr. Sisi Zlatanova
Prof. Dr. Maria Antonia Brovelli
Prof. Dr. Monika Sester
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.

Keywords

  • Multi-dimensional
  • Geospatial big data processing
  • City analytics
  • Advanced visualization and simulation
  • Virtual/augmented reality
  • Sensors/Internet of Things
  • Geosocial/crowdsourced data
  • Machine/deep learning
  • 3D modeling/BIM
  • Spatial data for traffic applications
  • Indoor mapping/navigation

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (10 papers)

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Review

29 pages, 7677 KiB  
Review
Extended Reality in Spatial Sciences: A Review of Research Challenges and Future Directions
by Arzu Çöltekin, Ian Lochhead, Marguerite Madden, Sidonie Christophe, Alexandre Devaux, Christopher Pettit, Oliver Lock, Shashwat Shukla, Lukáš Herman, Zdeněk Stachoň, Petr Kubíček, Dajana Snopková, Sergio Bernardes and Nicholas Hedley
ISPRS Int. J. Geo-Inf. 2020, 9(7), 439; https://doi.org/10.3390/ijgi9070439 - 15 Jul 2020
Cited by 178 | Viewed by 22460
Abstract
This manuscript identifies and documents unsolved problems and research challenges in the extended reality (XR) domain (i.e., virtual (VR), augmented (AR), and mixed reality (MR)). The manuscript is structured to include technology, design, and human factor perspectives. The text is visualization/display-focused, [...] Read more.
This manuscript identifies and documents unsolved problems and research challenges in the extended reality (XR) domain (i.e., virtual (VR), augmented (AR), and mixed reality (MR)). The manuscript is structured to include technology, design, and human factor perspectives. The text is visualization/display-focused, that is, other modalities such as audio, haptic, smell, and touch, while important for XR, are beyond the scope of this paper. We further narrow our focus to mainly geospatial research, with necessary deviations to other domains where these technologies are widely researched. The main objective of the study is to provide an overview of broader research challenges and directions in XR, especially in spatial sciences. Aside from the research challenges identified based on a comprehensive literature review, we provide case studies with original results from our own studies in each section as examples to demonstrate the relevance of the challenges in the current research. We believe that this paper will be of relevance to anyone who has scientific interest in extended reality, and/or uses these systems in their research. Full article
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
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20 pages, 341 KiB  
Review
State-of-the-Art Geospatial Information Processing in NoSQL Databases
by Dongming Guo and Erling Onstein
ISPRS Int. J. Geo-Inf. 2020, 9(5), 331; https://doi.org/10.3390/ijgi9050331 - 19 May 2020
Cited by 36 | Viewed by 8160
Abstract
Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival [...] Read more.
Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages. Full article
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
31 pages, 5876 KiB  
Review
A Review of Techniques for 3D Reconstruction of Indoor Environments
by Zhizhong Kang, Juntao Yang, Zhou Yang and Sai Cheng
ISPRS Int. J. Geo-Inf. 2020, 9(5), 330; https://doi.org/10.3390/ijgi9050330 - 19 May 2020
Cited by 129 | Viewed by 15875
Abstract
Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper [...] Read more.
Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model. Recently, there has been an increasing amount of research related to indoor environment reconstruction. Therefore, this paper reviews the state-of-the-art techniques for the three-dimensional (3D) reconstruction of indoor environments. First, some of the available benchmark datasets for 3D reconstruction of indoor environments are described and discussed. Then, data collection of 3D indoor spaces is briefly summarized. Furthermore, an overview of the geometric, semantic, and topological reconstruction of the indoor environment is presented, where the existing methodologies, advantages, and disadvantages of these three reconstruction types are analyzed and summarized. Finally, future research directions, including technique challenges and trends, are discussed for the purpose of promoting future research interest. It can be concluded that most of the existing indoor environment reconstruction methods are based on the strong Manhattan assumption, which may not be true in a real indoor environment, hence limiting the effectiveness and robustness of existing indoor environment reconstruction methods. Moreover, based on the hierarchical pyramid structures and the learnable parameters of deep-learning architectures, multi-task collaborative schemes to share parameters and to jointly optimize each other using redundant and complementary information from different perspectives show their potential for the 3D reconstruction of indoor environments. Furthermore, indoor–outdoor space seamless integration to achieve a full representation of both interior and exterior buildings is also heavily in demand. Full article
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
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19 pages, 497 KiB  
Review
A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities
by Hung Cao and Monica Wachowicz
ISPRS Int. J. Geo-Inf. 2020, 9(4), 272; https://doi.org/10.3390/ijgi9040272 - 21 Apr 2020
Cited by 7 | Viewed by 3600
Abstract
The proliferation of Internet of Things (IoT) systems has received much attention from the research community, and it has brought many innovations to smart cities, particularly through the Internet of Moving Things (IoMT). The dynamic geographic distribution of IoMT devices enables the devices [...] Read more.
The proliferation of Internet of Things (IoT) systems has received much attention from the research community, and it has brought many innovations to smart cities, particularly through the Internet of Moving Things (IoMT). The dynamic geographic distribution of IoMT devices enables the devices to sense themselves and their surroundings on multiple spatio-temporal scales, interact with each other across a vast geographical area, and perform automated analytical tasks everywhere and anytime. Currently, most of the geospatial applications of IoMT systems are developed for abnormal detection and control monitoring. However, it is expected that, in the near future, optimization and prediction tasks will have a larger impact on the way citizens interact with smart cities. This paper examines the state of the art of IoMT systems and discusses their crucial role in supporting anticipatory learning. The maximum potential of IoMT systems in future smart cities can be fully exploited in terms of proactive decision making and decision delivery via an anticipatory action/feedback loop. We also examine the challenges and opportunities of anticipatory learning for IoMT systems in contrast to GIS. The holistic overview provided in this paper highlights the guidelines and directions for future research on this emerging topic. Full article
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
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31 pages, 3300 KiB  
Review
A Review of Geospatial Semantic Information Modeling and Elicitation Approaches
by Margarita Kokla and Eric Guilbert
ISPRS Int. J. Geo-Inf. 2020, 9(3), 146; https://doi.org/10.3390/ijgi9030146 - 1 Mar 2020
Cited by 29 | Viewed by 6514
Abstract
The present paper provides a review of two research topics that are central to geospatial semantics: information modeling and elicitation. The first topic deals with the development of ontologies at different levels of generality and formality, tailored to various needs and uses. The [...] Read more.
The present paper provides a review of two research topics that are central to geospatial semantics: information modeling and elicitation. The first topic deals with the development of ontologies at different levels of generality and formality, tailored to various needs and uses. The second topic involves a set of processes that aim to draw out latent knowledge from unstructured or semi-structured content: semantic-based extraction, enrichment, search, and analysis. These processes focus on eliciting a structured representation of information in various forms such as: semantic metadata, links to ontology concepts, a collection of topics, etc. The paper reviews the progress made over the last five years in these two very active areas of research. It discusses the problems and the challenges faced, highlights the types of semantic information formalized and extracted, as well as the methodologies and tools used, and identifies directions for future research. Full article
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
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25 pages, 2605 KiB  
Review
3D Land Administration: A Review and a Future Vision in the Context of the Spatial Development Lifecycle
by Eftychia Kalogianni, Peter van Oosterom, Efi Dimopoulou and Christiaan Lemmen
ISPRS Int. J. Geo-Inf. 2020, 9(2), 107; https://doi.org/10.3390/ijgi9020107 - 13 Feb 2020
Cited by 58 | Viewed by 6480
Abstract
Land Administration practices worldwide rely mainly on 2D-based systems to define legal and other spatial boundaries related to land interests. However, the built environment is increasingly becoming spatially complex. Land administrators are challenged by an unprecedented demand to utilise space above and below [...] Read more.
Land Administration practices worldwide rely mainly on 2D-based systems to define legal and other spatial boundaries related to land interests. However, the built environment is increasingly becoming spatially complex. Land administrators are challenged by an unprecedented demand to utilise space above and below earth’s surface. The relationships between people and land in vertical space can no longer be unambiguously represented in 2D. In addition, the current societal demand for sustainability in a collaborative environment and a lifecycle-thinking, is driving the need to integrate independent systems with standalone databases and methodologies, associated with different aspects of the Spatial Development lifeCycle (SDC). Land Administration Systems (LASs) are an important component of the SDC. Today, a LAS is often mandated and managed as a domain in isolation. Interaction and data reuse with the other phases of the SDC is limited and far from optimal. It is expected that effective 3D data collaboration, sharing, and reuse across the sectors and disciplines in the lifecycle will enable new ways of data harmonisation and use in this complex environment; will improve efficiency of design and data acquisition, as well as data quality (in relation to specific regulations); and will minimise inconsistencies and data loss within information flows. Overall, a cross-sectoral approach is directed towards improving the current state of the Land Administration (LA) domain. This paper consists of two parts. In the first, a review of the current situation, with respect to LASs is presented, concluding the needs for improvement in terms of effectiveness and consistency. In the second part, the vision for the future of LASs is introduced in a wider context, and as an important phase in the SDC, with regards to legal, technical, and organisational aspects. In this part, the needs and considerations that result from the evolving environment and the emerging technological advances are addressed, with a view to discussing a cross-sector approach to collect, maintain, reuse, and share 3D data. In such a cross-sectoral approach, various interoperability issues appear, making it necessary to introduce and use standards. In this respect, the ISO 19152:2012 Land Administration Domain Model (LADM) in its current Edition I, as well as in Edition II (expected in 2022) may serve as the standardised core structure of a 3D LAS, with respect to its role as further presented in this paper. In parallel, the evolution of the Building Information Modelling (BIM) in the design and construction industry, as well as the fact that BIM plays a central role in the life cycle of development projects, are well recognized. Emphasis is given on feasible reuse of BIM/IFC (Industry Foundation Class) data in a 3D LAS. Those considerations are addressed through a web-based system architecture for a future 3D LAS, thereby attempting to integrate heterogeneous systems in the SDC. Full article
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
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Graphical abstract

20 pages, 1569 KiB  
Review
Geospatial Data Management Research: Progress and Future Directions
by Martin Breunig, Patrick Erik Bradley, Markus Jahn, Paul Kuper, Nima Mazroob, Norbert Rösch, Mulhim Al-Doori, Emmanuel Stefanakis and Mojgan Jadidi
ISPRS Int. J. Geo-Inf. 2020, 9(2), 95; https://doi.org/10.3390/ijgi9020095 - 4 Feb 2020
Cited by 106 | Viewed by 18794
Abstract
Without geospatial data management, today’s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, [...] Read more.
Without geospatial data management, today’s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis. Full article
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
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30 pages, 5804 KiB  
Review
Open Geospatial Software and Data: A Review of the Current State and A Perspective into the Future
by Serena Coetzee, Ivana Ivánová, Helena Mitasova and Maria Antonia Brovelli
ISPRS Int. J. Geo-Inf. 2020, 9(2), 90; https://doi.org/10.3390/ijgi9020090 - 1 Feb 2020
Cited by 88 | Viewed by 15657
Abstract
All over the world, organizations are increasingly considering the adoption of open source software and open data. In the geospatial domain, this is no different, and the last few decades have seen significant advances in this regard. We review the current state of [...] Read more.
All over the world, organizations are increasingly considering the adoption of open source software and open data. In the geospatial domain, this is no different, and the last few decades have seen significant advances in this regard. We review the current state of open source geospatial software, focusing on the Open Source Geospatial Foundation (OSGeo) software ecosystem and its communities, as well as three kinds of open geospatial data (collaboratively contributed, authoritative and scientific). The current state confirms that openness has changed the way in which geospatial data are collected, processed, analyzed, and visualized. A perspective on future developments, informed by responses from professionals in key organizations in the global geospatial community, suggests that open source geospatial software and open geospatial data are likely to have an even more profound impact in the future. Full article
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
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28 pages, 9450 KiB  
Review
Spaces in Spatial Science and Urban Applications—State of the Art Review
by Sisi Zlatanova, Jinjin Yan, Yijing Wang, Abdoulaye Diakité, Umit Isikdag, George Sithole and Jack Barton
ISPRS Int. J. Geo-Inf. 2020, 9(1), 58; https://doi.org/10.3390/ijgi9010058 - 20 Jan 2020
Cited by 40 | Viewed by 11331
Abstract
In spatial science and urban applications, “space" is presented by multiple disciplines as a notion referencing our living environment. Space is used as a general term to help understand particular characteristics of the environment. However, the definition and perception of space varies and [...] Read more.
In spatial science and urban applications, “space" is presented by multiple disciplines as a notion referencing our living environment. Space is used as a general term to help understand particular characteristics of the environment. However, the definition and perception of space varies and these variations have to be harmonised. For example, space may have diverse definitions and classification, the same environment may be abstracted/modelled by contradicting notions of space, which can lead to inconsistencies and misunderstandings. In this paper, we seek to investigate and document the state-of-the-art in the research of “space” regarding its definition, classification, modelling and utilization (2D/3D) in spatial sciences and urban applications. We focus on positioning, navigation, building micro-climate and thermal comfort, landscape, urban planning and design, urban heat island, interior design and planning, transportation and intelligent space. We review 147 research papers, technical reports and on-line resources. We compare the presented space concepts with respect to five criteria—classification, boundary, modelling components, use of standards and granularity. The review inventory is intended for both scientists and professionals in the spatial industry, such as companies, national mapping agencies and governments, and aim to provide a reference to better understand and employ the “space” while working across disciplines. Full article
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
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25 pages, 8944 KiB  
Review
Traffic Regulator Detection and Identification from Crowdsourced Data—A Systematic Literature Review
by Stefania Zourlidou and Monika Sester
ISPRS Int. J. Geo-Inf. 2019, 8(11), 491; https://doi.org/10.3390/ijgi8110491 - 31 Oct 2019
Cited by 12 | Viewed by 3660
Abstract
Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of [...] Read more.
Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this article is motivated by this fact. The topic of this systematic literature review (SLR) is the detection and recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right of way priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourced data. More particularly, the aim of this study is (1) to identify the range of detected and recognised regulatory types by crowdsensing means, (2) to indicate the different classification techniques that can be used for these two tasks, (3) to assess the performance of different methods, as well as (4) to identify important aspects of the applicability of these methods. The two largest databases of peer-reviewed literature were used to locate relevant research studies and after different screening steps eleven articles were selected for review. Two major findings were concluded—(a) most regulator types can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) under the current progress on the field, no study can be reproduced for comparative purposes nor can solely rely on open data sources due to lack of publicly available datasets and ground truth maps. Future research directions are highlighted as possible extensions of the reviewed studies. Full article
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
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