Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

Jovanović, Dušan; Milovanov, S. E.; Ruskovski, Igor; Govedarica, Miro; Sladić, Dubravka; Radulović, Aleksandra; Pajic, Vladimir

doi:10.3390/ijgi9080476

Cited by 58 publications

(36 citation statements)

References 63 publications

(64 reference statements)

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“…Our rationale was that, despite the clear differences between a city and a university, the results obtained using a smart university platform can, to a certain extent, be relevant to a city, provided that the platform serves an extensive community, such as that of a university campus. This assumption is supported by previous studies according to which a university campus can be used as a less complex model of a city [12][13][14][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79]. Moreover, we sought previous works directly related to our hypothesis according to which the existence of a global framework, that includes a methodology for adding new services to a smart university platform and guarantees both ease of integration and strategic alignment, would make it easier to realise these types of projects.…”

Section: Background and Motivationmentioning

confidence: 53%

“…A number of previous publications describe the university as a small-scale city with the potential to be used as a model for scientific purposes [12][13][14]. We can affirm that a university campus represents an appropriate mock-up for a smart city initiative: it is representative and, in addition to serving as an example, guide and case study with which to develop viable projects in our cities, the partial results would be useful in themselves to effectively and efficiently improve the quality of life of a large number of citizens.…”

Section: Introductionmentioning

confidence: 99%

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Modelling and Implementing Smart Universities: An IT Conceptual Framework

2021

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The smart city concept has been gaining momentum in the scientific community because of its potentially huge impact on citizens’ quality of life. However, expectations have not yet been met in practice. This is firstly due to the sheer breadth of such projects and secondly to the lack of methodologies available to guide the development of flexible and sustainable platforms over time. In this work, we propose to address these issues by using a university campus as a less complex mock-up version of a city. Despite differences between them, we find services that are common to both, and a medium-sized city’s population is comparable to that of a university community. We propose an IT conceptual framework to model and implement smart university projects, which supports the design of a platform that is both in line with the strategic plans of universities and is flexible, sustainable, stable, and sufficiently modular to support the addition of different value-added services over the years. Our framework is based on a service provision model materialised in an IT architecture and managed following a methodology to integrate IT components that ensure the insertion of new, smart initiatives of value to the community, aligned with the university’s needs, via a value-added service planning process. The results are presented in the University of Alicante case study and the SmartUA project.

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Section: Background and Motivationmentioning

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Modelling and Implementing Smart Universities: An IT Conceptual Framework

2021

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“…OpenTripPlanner (OTP) was the OSM routing engine selected for use with our proof of concept implementation. In their research, Jovanović et al [67] recommended 3DCityDB as a platform for storage of transformed and adopted IndoorGML data, which provides good connectivity to other visualization (GIS and VR) platforms. For the implementation of the geosensors submodule, two standards (IoT and SWE) were considered.…”

Section: Discussionmentioning

confidence: 99%

Big Data in Smart City: Management Challenges

et al. 2021

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Smart cities use digital technologies such as cloud computing, Internet of Things, or open data in order to overcome limitations of traditional representation and exchange of geospatial data. This concept ensures a significant increase in the use of data to establish new services that contribute to better sustainable development and monitoring of all phenomena that occur in urban areas. The use of the modern geoinformation technologies, such as sensors for collecting different geospatial and related data, requires adequate storage options for further data analysis. In this paper, we suggest the biG dAta sMart cIty maNagEment SyStem (GAMINESS) that is based on the Apache Spark big data framework. The model of the GAMINESS management system is based on the principles of the big data modeling, which differs greatly from standard databases. This approach provides the ability to store and manage huge amounts of structured, semi-structured, and unstructured data in real time. System performance is increasing to a higher level by using the process parallelization explained through the five V principles of the big data paradigm. The existing solutions based on the five V principles are focused only on the data visualization, not the data themselves. Such solutions are often limited by different storage mechanisms and by the ability to perform complex analyses on large amounts of data with expected performance. The GAMINESS management system overcomes these disadvantages by conversion of smart city data to a big data structure without limitations related to data formats or use standards. The suggested model contains two components: a geospatial component and a sensor component that are based on the CityGML and the SensorThings standards. The developed model has the ability to exchange data regardless of the used standard or the data format into proposed Apache Spark data framework schema. The verification of the proposed model is done within the case study for the part of the city of Novi Sad.

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“…With the existence of 3D data acquisition technology that can be used at the end-user level (i.e., LiDAR), 3D data has become accessible, and it simplifies the process of modeling 3D objects. Currently, the use of 3D spatial objects is used in various applications such as environmental modeling (Ledoux, 2008;Ujang et al, 2018), GIS-based Building Information Modeling (Atazadeh et al, 2017;Basir et al, 2018;Döllner and Hagedorn, 2007) and 3D city modeling (Biljecki et al, 2017;Jovanović et al, 2020;Salleh et al, 2021). Even so, there are still various issues as the 3D integration of objects between various fields sometimes require further assessments.…”

Section: Introductionmentioning

confidence: 99%

Data Interoperability of Building Information Modeling and Geographic Information System in Construction Industry

Basir

Ujang

Majid

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Application of Building Information Modeling (BIM) in construction industry has been applied for many years back. This because BIM can provide a better advantage in construction industry in term of controlling and managing construction project during their life cycle. The advantages that can be provide by BIM is focusing on the indoor planning tasks. But, when the construction project involves, besides indoor planning, outdoor planning also is important part that need to be look up. To cover the outdoor planning in construction project, Geographic Information System (GIS) need to be applied. GIS can overcome this problem because GIS mainly for outdoor planning by using their spatial analysis. GIS can offer a high degree of geospatial information and can provide the detailed geometrical and semantic information of building to assisted across improve automation. Towards produce the improved preparation in construction project, BIM and GIS should be integrated. To integrate both domains, the data interoperability between them need to be investigate because they used different data standard. This study focusses on solving the data interoperability through the data integration between BIM and GIS to solve the problem of data mismatch and data missing during data translation process. Industry Foundation Classes (IFC) was used as a data standard to perform the data integration between BIM and GIS. The outcomes from this study show that when the data interoperability applied between BIM and GIS, the problem above can be solved, and the data dimension and their coordinate system also can be control.

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Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

Cited by 58 publications

References 63 publications

Modelling and Implementing Smart Universities: An IT Conceptual Framework

Modelling and Implementing Smart Universities: An IT Conceptual Framework

Big Data in Smart City: Management Challenges

Data Interoperability of Building Information Modeling and Geographic Information System in Construction Industry

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