Developing a CityGML-based Graph Data Model for Utility Infrastructure in Smart Cities

Graph data models are essential for the development of smart cities, where interconnected systems such as utility networks, transportation, and IoT devices must function cohesively. The complexity of smart city infrastructure necessitates 3D data structures capable of managing intricate relationship...

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Main Authors: E. Javaherian Pour, B. Atazadeh, A. Rajabifard, S. Sabri
Format: Article
Language:English
Published: Copernicus Publications 2025-07-01
Series:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://isprs-annals.copernicus.org/articles/X-G-2025/405/2025/isprs-annals-X-G-2025-405-2025.pdf
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author E. Javaherian Pour
B. Atazadeh
A. Rajabifard
S. Sabri
author_facet E. Javaherian Pour
B. Atazadeh
A. Rajabifard
S. Sabri
author_sort E. Javaherian Pour
collection DOAJ
description Graph data models are essential for the development of smart cities, where interconnected systems such as utility networks, transportation, and IoT devices must function cohesively. The complexity of smart city infrastructure necessitates 3D data structures capable of managing intricate relationships, dynamic environments, and high connectivity across diverse systems. Graph data models are particularly suited for this purpose, as they offer an integrated 3D digital representation of urban complexity and interconnectivity. This study employs the Labelled Property Graph (LPG) framework to develop a 3D graph data model based on the Utility Network Application Domain Extension (ADE) of the CityGML standard. The proposed approach enhances utility network data management, enabling advanced analyses such as connectivity assessment and pathfinding. The developed graph data model is evaluated in terms of constraint preservation, information integrity, and connection realism. Results demonstrate that the model accurately represents real-world utility network structures while preventing data loss and duplication.
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issn 2194-9042
2194-9050
language English
publishDate 2025-07-01
publisher Copernicus Publications
record_format Article
series ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
spelling doaj-art-f0fbea1ddfa64d4dbed262beb821ffa32025-08-20T03:17:35ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502025-07-01X-G-202540541210.5194/isprs-annals-X-G-2025-405-2025Developing a CityGML-based Graph Data Model for Utility Infrastructure in Smart CitiesE. Javaherian Pour0B. Atazadeh1A. Rajabifard2S. Sabri3The Centre for Spatial Data Infrastructure and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Victoria 3010, AustraliaThe Centre for Spatial Data Infrastructure and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Victoria 3010, AustraliaThe Centre for Spatial Data Infrastructure and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Victoria 3010, AustraliaUrban Digital Twin Lab, School of Modelling Simulation and Training, University of Central Florida, Orlando, USAGraph data models are essential for the development of smart cities, where interconnected systems such as utility networks, transportation, and IoT devices must function cohesively. The complexity of smart city infrastructure necessitates 3D data structures capable of managing intricate relationships, dynamic environments, and high connectivity across diverse systems. Graph data models are particularly suited for this purpose, as they offer an integrated 3D digital representation of urban complexity and interconnectivity. This study employs the Labelled Property Graph (LPG) framework to develop a 3D graph data model based on the Utility Network Application Domain Extension (ADE) of the CityGML standard. The proposed approach enhances utility network data management, enabling advanced analyses such as connectivity assessment and pathfinding. The developed graph data model is evaluated in terms of constraint preservation, information integrity, and connection realism. Results demonstrate that the model accurately represents real-world utility network structures while preventing data loss and duplication.https://isprs-annals.copernicus.org/articles/X-G-2025/405/2025/isprs-annals-X-G-2025-405-2025.pdf
spellingShingle E. Javaherian Pour
B. Atazadeh
A. Rajabifard
S. Sabri
Developing a CityGML-based Graph Data Model for Utility Infrastructure in Smart Cities
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
title Developing a CityGML-based Graph Data Model for Utility Infrastructure in Smart Cities
title_full Developing a CityGML-based Graph Data Model for Utility Infrastructure in Smart Cities
title_fullStr Developing a CityGML-based Graph Data Model for Utility Infrastructure in Smart Cities
title_full_unstemmed Developing a CityGML-based Graph Data Model for Utility Infrastructure in Smart Cities
title_short Developing a CityGML-based Graph Data Model for Utility Infrastructure in Smart Cities
title_sort developing a citygml based graph data model for utility infrastructure in smart cities
url https://isprs-annals.copernicus.org/articles/X-G-2025/405/2025/isprs-annals-X-G-2025-405-2025.pdf
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AT batazadeh developingacitygmlbasedgraphdatamodelforutilityinfrastructureinsmartcities
AT arajabifard developingacitygmlbasedgraphdatamodelforutilityinfrastructureinsmartcities
AT ssabri developingacitygmlbasedgraphdatamodelforutilityinfrastructureinsmartcities