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Geospatial Artificial Intelligence, GIS or BIM: Applications for Construction, Smart...
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Geospatial Artificial Intelligence, GIS or BIM: Applications for Construction, Smart City and Urban Planning

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 20937

Special Issue Editors

Dr. Sara Shirowzhan

E-Mail Website
Guest Editor
City Futures Research Centre, School of Built Environment, University of New South Wales, Kensington, Sydney, NSW 2052, Australia
Interests: sensing technologies; AI; machine learning; advanced GIS; BIM; digital twins; city analytics methods; digital construction; smart cities; smart construction
Special Issues, Collections and Topics in MDPI journals
Dr. Aaron Costin

E-Mail Website
Guest Editor
M. E. Rinker, Sr. School of Construction Management, University of Florida, Gainesville, FL 32611, USA
Interests: building information modelling (BIM); design computing; construction safety and productivity; cyberphysical systems; interoperability; Internet of Things; smart cities
Special Issues, Collections and Topics in MDPI journals
Dr. Samad Sepasgozar

E-Mail Website
Guest Editor
School of Built Environment, UNSW Sydney, Sydney, NSW 1466, Australia
Interests: sustainability; energy efficiency; artificial intelligence; smart city; digital twin; applications of the internet of things; advanced GIS; LiDAR; BIM; digital technology in infrastructure; mixed reality applications; information and communication technology; spatial analysis and visualization; authentic education
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Leading organisations tend to implement artificial intelligence (AI) including deep learning, machine learning, using geospatial and location data to automate design, operation, monitoring, and predictive modelling. This happens at different scales, such as city, organisation, projects or buildings. This Special Issue invites all researchers to share their scholarly work concerning the development of advanced digital technologies which may help the implementation of smart cities and/or intelligent construction.

Since geospatial technology is rapidly advancing, there is an urgent need to identify and develop new applications. At the same time, all technical challenges of these technologies should be addressed to foster the technology uptake rate. The Special Issue welcomes all technical endeavours, technology developments, implementation case studies and experimentations related to geospatial information systems and other compatible technologies carried out to address one of the many challenges encountered by smart cities, smart construction, infrastructure maintenance, and disaster management.

This Special Issue invites all researchers to share their scholarly work concerning the development of advanced technologies that may facilitate the implementation of smart cities. It will cover topics such as:

  • Visualisation case studies and frameworks;
  • IoT and smart city ontologies;
  • Semantic web technologies;
  • Geospatial data acquisition for smart cities;
  • 3D geometry ontologies;
  • Use of Geo-ICT for planning smart cities;
  • Semantic sensor ontologies (SSN);
  • Geospatial database management;
  • Big data analytics for smart cities;
  • Real-time location intelligence;
  • Use of geospatial data for smart urban management, particularly infrastructure planning, construction, and maintenance;
  • Real-time monitoring of urban environment including air, water and noise;
  • Use of geospatial data for planning and building resilient cities; including security and disaster responses.
Dr. Sara Shirowzhan
Dr. Aaron Costin
Dr. Samad Sepasgozar

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 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

  • GIS
  • BIM
  • semantic sensor ontologies (SSN)
  • industry foundation classes (IFC)
  • Geo-ICT
  • Artificial Intelligence
  • deep learning
  • machine learning

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

Published Papers (5 papers)

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Research

23 pages, 3623 KiB  
Article
Analyzing Contextual Linking of Heterogeneous Information Models from the Domains BIM and UIM
by Stefan F. Beck, Jimmy Abualdenien, Ihab H. Hijazi, André Borrmann and Thomas H. Kolbe
ISPRS Int. J. Geo-Inf. 2021, 10(12), 807; https://doi.org/10.3390/ijgi10120807 - 30 Nov 2021
Cited by 18 | Viewed by 3458
Abstract
Information models from the domains Building Information Modeling (BIM) and Urban Information Modeling (UIM) are generally considered as information silos due to their heterogeneous character. These information silos can be bridged through linking where corresponding objects are identified and linked subsequently. However, whether [...] Read more.
Information models from the domains Building Information Modeling (BIM) and Urban Information Modeling (UIM) are generally considered as information silos due to their heterogeneous character. These information silos can be bridged through linking where corresponding objects are identified and linked subsequently. However, whether two objects are considered as corresponding might depend on the scenario for which the links are created. The dependency of the link creation and the scenario refers to the term contextual linking and is analyzed in this paper with respect to building and city models. Therefore, different situational aspects influencing the link creation are discussed. Afterwards, the issue of contextual linking is demonstrated based on three different integration scenarios. In summary, this paper has three major outcomes: First, this paper introduces an application-oriented perspective on information integration and emphasize the role of the application when linking heterogeneous information models. Second, this paper shows that linking heterogeneous information models from the domains BIM and UIM at instance level depends on the scenario. Third, the results of the discourse about contextual linking serve as a framework supporting the design and development of artifacts for linking heterogeneous information models from the domains BIM and UIM. Full article
(This article belongs to the Special Issue Geospatial Artificial Intelligence, GIS or BIM: Applications for Construction, Smart City and Urban Planning)
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27 pages, 7439 KiB  
Article
A Smart Tourism Recommendation Algorithm Based on Cellular Geospatial Clustering and Multivariate Weighted Collaborative Filtering
by Xiao Zhou, Jiangpeng Tian, Jian Peng and Mingzhan Su
ISPRS Int. J. Geo-Inf. 2021, 10(9), 628; https://doi.org/10.3390/ijgi10090628 - 19 Sep 2021
Cited by 8 | Viewed by 3478
Abstract
Tourist attraction and tour route recommendation are the key research highlights in the field of smart tourism. Currently, the existing recommendation algorithms encounter certain problems when making decisions regarding tourist attractions and tour routes. This paper presents a smart tourism recommendation algorithm based [...] Read more.
Tourist attraction and tour route recommendation are the key research highlights in the field of smart tourism. Currently, the existing recommendation algorithms encounter certain problems when making decisions regarding tourist attractions and tour routes. This paper presents a smart tourism recommendation algorithm based on a cellular geospatial clustering and weighted collaborative filtering. The problems are analyzed and concluded, and then the research ideas and methods to solve the problems are introduced. Aimed at solving the problems, the tourist attraction recommendation model is set up based on a cellular geographic space generating model and a weighted collaborative filtering model. According to the matching degree between the tourists’ interest needs and tourist attraction feature attributes, a precise tourist attraction recommendation is obtained. In combination with the geospatial attributes of the tourist destination, the spatial adjacency clustering model based on the cellular space generating algorithm is set up, and then the weighted model is introduced for the collaborative filtering recommendation algorithm, which ensures that the recommendation result precisely matches the tourists’ needs. Providing precise results, the optimal tour route recommendation model based on the precise tourist attraction approach vector algorithm is set up. The approach vector algorithm is used to search the optimal route between two POIs under the condition of multivariate traffic modes to provide the tourists with the best motive benefits. To verify the feasibility and advantages of the algorithm, this paper designs a sample experiment and analyzes the resulting data to obtain the relevant conclusion. Full article
(This article belongs to the Special Issue Geospatial Artificial Intelligence, GIS or BIM: Applications for Construction, Smart City and Urban Planning)
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22 pages, 8117 KiB  
Article
Urban Overheating Assessment through Prediction of Surface Temperatures: A Case Study of Karachi, Pakistan
by Bilal Aslam, Ahsen Maqsoom, Nauman Khalid, Fahim Ullah and Samad Sepasgozar
ISPRS Int. J. Geo-Inf. 2021, 10(8), 539; https://doi.org/10.3390/ijgi10080539 - 11 Aug 2021
Cited by 34 | Viewed by 5519
Abstract
Global climate has been radically affected by the urbanization process in recent years. Karachi, Pakistan’s economic hub, is also showing signs of swift urbanization. Owing to the construction of infrastructure projects under the China-Pakistan Economic Corridor (CPEC) and associated urbanization, Karachi’s climate has [...] Read more.
Global climate has been radically affected by the urbanization process in recent years. Karachi, Pakistan’s economic hub, is also showing signs of swift urbanization. Owing to the construction of infrastructure projects under the China-Pakistan Economic Corridor (CPEC) and associated urbanization, Karachi’s climate has been significantly affected. The associated replacement of natural surfaces by anthropogenic materials results in urban overheating and increased local temperatures leading to serious health issues and higher air pollution. Thus, these temperature changes and urban overheating effects must be addressed to minimize their impact on the city’s population. For analyzing the urban overheating of Karachi city, LST (land surface temperature) is assessed in the current study, where data of the past 20 years (2000–2020) is used. For this purpose, remote sensing data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors were utilized. The long short-term memory (LSTM) model was utilized where the road density (RD), elevation, and enhanced vegetation index (EVI) are used as input parameters. Upon comparing estimated and measured LST, the values of mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) are 0.27 K, 0.237, and 0.15% for January, and 0.29 K, 0.261, and 0.13% for May, respectively. The low MAE, MSE, and MAPE values show a higher correlation between the predicted and observed LST values. Moreover, results show that more than 90% of the pixel data falls in the least possible error range of −1 K to +1 K. The MAE, MSE and MAPE values for Support Vector Regression (SVR) are 0.52 K, 0.453 and 0.18% and 0.76 K, 0.873, and 0.26%. The current model outperforms previous studies, shows a higher accuracy, and depicts greater reliability to predict the actual scenario. In the future, based on the accurate LST results from this model, city planners can propose mitigation strategies to reduce the harmful effects of urban overheating and associated Urban Heat Island effects (UHI). Full article
(This article belongs to the Special Issue Geospatial Artificial Intelligence, GIS or BIM: Applications for Construction, Smart City and Urban Planning)
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23 pages, 5210 KiB  
Article
Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada
by Abul Azad and Xin Wang
ISPRS Int. J. Geo-Inf. 2021, 10(6), 358; https://doi.org/10.3390/ijgi10060358 - 23 May 2021
Cited by 13 | Viewed by 2881
Abstract
Land use and transportation planning have a significant impact on the performance of cities’ traffic conditions and the quality of people’s lives. The changing characteristics of land use will affect and challenge how a city is able to manage, organize, and plan for [...] Read more.
Land use and transportation planning have a significant impact on the performance of cities’ traffic conditions and the quality of people’s lives. The changing characteristics of land use will affect and challenge how a city is able to manage, organize, and plan for new developments and transportation. These challenges can be better addressed with effective methods of monitoring and predicting, which can enable optimal efficiency in how a growing city like Calgary, Canada, can perform. Using ontology in land use planning is a new initiative currently being researched and explored. In this regard, ontology incorporates relationships between the various entities of land use. The aim of this study is to present Land Use Change Ontology (LUCO) with a deep neural network for traffic prediction. We present a Land Use Change Ontology (LUCO) approach, using expressions of how the semantics of land use changes relate to the integration of temporal land use information. This study examines the City of Calgary’s land use data from the years 2001, 2010, and 2015. In applying the LUCO approach to test data, experimental outcomes indicated that from 2001 to 2015 residential land use increased by 30% and open space decreased by 40%. Forecasting traffic is increasingly essential for successful traffic modelling, operations, and management. However, traditional means for predicting traffic flow have largely assumed restrictive model architectures that have not controlled for the amounts of land use change. Inspired by deep learning methods and effective data mining computing capabilities, this paper introduces the deep learning Recurrent Neural Network (RNN) to predict traffic while considering the impact of land use change. The RNN was successful in learning the features of traffic flow under various land use change situations. Experimental results indicated that, with the consideration of LUCO, the deep learning predictors had better accuracy when compared with other existing models. Success of our modeling approach indicates that cities could apply this modeling approach to make land use transportation planning more efficient. Full article
(This article belongs to the Special Issue Geospatial Artificial Intelligence, GIS or BIM: Applications for Construction, Smart City and Urban Planning)
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18 pages, 2201 KiB  
Article
Mountainous City Featured Landscape Planning Based on GIS-AHP Analytical Method
by Yanlong Liu and Li Li
ISPRS Int. J. Geo-Inf. 2020, 9(4), 211; https://doi.org/10.3390/ijgi9040211 - 30 Mar 2020
Cited by 11 | Viewed by 3446
Abstract
In order to take full advantage of the landscape resources in the city’s featured landscape planning, and mutually integrate ecological green land with city space, this paper takes the mountainous city, Qianxi County, as the research subject to conduct an ecological sensitivity analysis [...] Read more.
In order to take full advantage of the landscape resources in the city’s featured landscape planning, and mutually integrate ecological green land with city space, this paper takes the mountainous city, Qianxi County, as the research subject to conduct an ecological sensitivity analysis with the GIS space analytical method, while adopting the Analytic Hierarchy Process (AHP) method to find a landscape resource assessment system for Qianxi County. Based on the analysis of the mountainous city landscape pattern characteristics and ecological adaptability, the paper combines with the landscape planning practice in Qianxi County and starts from the ecological pattern construction and urban landscape resource assessment to expound the methodological guidance function of the GIS-AHP analytical method for the mountainous city landscape planning. This method helps recognize the characteristics of the city landscape resources in an all-sided way that protects the city landscape, improves the use-value of the mountainous city landscape resources, integrates the city land area with the water area landscape’s green land and builds an ecological, cultural, and habitable mountainous city featured landscape pattern. Full article
(This article belongs to the Special Issue Geospatial Artificial Intelligence, GIS or BIM: Applications for Construction, Smart City and Urban Planning)
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