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

Special Issue Editors

Dr. Aaron Costin
E-Mail Website
Guest Editor
M. E. Rinker, Sr. School of Construction Management, University of Florida, Gainesville, FL, USA
Interests: building information modelling (BIM); design computing; construction safety and productivity; cyberphysical systems; interoperability; Internet of Things; smart cities
Dr. Samad M. E. Sepasgozar
E-Mail Website
Guest Editor
Faculty of Built Environment, University of New South Wales, Sydney, NSW 2052, 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
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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 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 1400 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

Published Papers (3 papers)

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Research

Article
Urban Overheating Assessment through Prediction of Surface Temperatures: A Case Study of Karachi, Pakistan
ISPRS Int. J. Geo-Inf. 2021, 10(8), 539; https://doi.org/10.3390/ijgi10080539 - 11 Aug 2021
Viewed by 324
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
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Article
Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada
ISPRS Int. J. Geo-Inf. 2021, 10(6), 358; https://doi.org/10.3390/ijgi10060358 - 23 May 2021
Viewed by 501
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
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Article
Mountainous City Featured Landscape Planning Based on GIS-AHP Analytical Method
ISPRS Int. J. Geo-Inf. 2020, 9(4), 211; https://doi.org/10.3390/ijgi9040211 - 30 Mar 2020
Cited by 2 | Viewed by 737
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
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