Automatic Transformation of Different Levels of Detail in 3D GIS City Models in CityGML

Deng, Yichuan; Cheng, Jack Chin Pang

doi:10.4018/ij3dim.2015070101

Cited by 3 publications

(4 citation statements)

References 28 publications

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“…For urban energy analysis, however, open data models do not generally include the geometric detailing required to represent individual buildings in their respective geographical contexts. As previously mentioned in Section 3, studies such as [115][116][117][118], do allow transforming CityGML building models; however, these are primarily limited to downscaling higher LoD models to lower ones. Moreover, the algorithms are mostly available with a restricted access.…”

Section: Discussionmentioning

confidence: 99%

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Open-Source Tool for Transforming CityGML Levels of Detail

et al. 2021

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Urban Building Energy Modelling (UBEM) requires adequate geometrical information to represent buildings in a 3D digital form. However, open data models usually lack essential information, such as building geometries, due to a lower granularity in available data. For heating demand simulations, this scarcity impacts the energy predictions and, thereby, questioning existing simulation workflows. In this paper, the authors present an open-source CityGML LoD Transformation (CityLDT) tool for upscaling or downscaling geometries of 3D spatial CityGML building models. With the current support of LoD0–2, this paper presents the adapted methodology and developed algorithms for transformations. Using the presented tool, the authors transform open CityGML datasets and conduct heating demand simulations in Modelica to validate the geometric processing of transformed building models.

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Section: Discussionmentioning

confidence: 99%

“…The presented approach in their article did not include any information over upscaling CityGML LoDs. Another method by Deng and Cheng [116] also presented an automatic transformation of different CityGML LoDs. In their workflow, a new exterior shell extraction algorithm is developed using a ray tracing-based algorithm.…”

Section: Aim Of Researchmentioning

confidence: 99%

Open-Source Tool for Transforming CityGML Levels of Detail

et al. 2021

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“…Such rules will make a real progress in 3D generalization techniques (Breunig & . As a recent contribution in this field, Cheng et al (2015) proposed a methodology framework for automatic transformation of different LoDs in CityGML.…”

Section: Multi Scale Representationmentioning

confidence: 99%

Collaborative 3D Modeling

Hajji

Billen

2016

International Journal of 3-D Information Modeling

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The need of 3D city models increases day by day. However, 3D modeling still faces some impediments to be generalized. Therefore, new solutions such as collaboration should be investigated. The paper presents a new vision of collaboration applied on 3D modeling through the definition of the concept of a 3D collaborative model. The paper highlights basic questions to be considered for the definition and the development of that model then argues the importance of reuse of 2D data as a promising solution to reconstruct 3D data and to upgrade to integrated 3D solutions in the future. This idea is supported by a case study, to demonstrate how 2D/2.5D data collected from different providers in Walloon region in Belgium can be integrated and reengineered to match the specifications of a 3D building model compatible with the CityGML standard.

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“…Second, the problem may be approached from the generalization perspective; 3D geoinformation at fine LODs may in fact be too complex for certain spatial analyses. Hence, the data are occasionally generalized to reduce complexity while attempting to preserve usability (Deng & Cheng, 2015). Insights into the performance of the LODs may help to achieve that balance: by generalizing the models to a point at which their complexity is sufficiently reduced but at the same time their usability is not compromised by the reduced LOD.…”

Section: Representation-induced Errormentioning

confidence: 99%

The effect of acquisition error and level of detail on the accuracy of spatial analyses

Biljecki

Heuvelink

Ledoux

et al. 2017

Cartography and Geographic Information Science

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There has been a great deal of research about errors in geographic information and how they affect spatial analyses. A typical GIS process introduces various types of errors at different stages, and such errors usually propagate into errors in the result of a spatial analysis. However, most studies consider only a single error type thus preventing the understanding of the interaction and relative contributions of different types of errors. We focus on the level of detail (LOD) and positional error, and perform a multiple error propagation analysis combining both types of error. We experiment with three spatial analyses (computing gross volume, envelope area, and solar irradiation of buildings) performed with procedurally generated 3D city models to decouple and demonstrate the magnitude of the two types of error, and to show how they individually and jointly propagate to the output of the employed spatial analysis. The most notable result is that in the considered spatial analyses the positional error has a much higher impact than the LOD. As a consequence, we suggest that it is pointless to acquire geoinformation of a fine LOD if the acquisition method is not accurate, and instead we advise focusing on the accuracy of the data. ARTICLE HISTORY

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Automatic Transformation of Different Levels of Detail in 3D GIS City Models in CityGML

Cited by 3 publications

References 28 publications

Open-Source Tool for Transforming CityGML Levels of Detail

Open-Source Tool for Transforming CityGML Levels of Detail

Collaborative 3D Modeling

The effect of acquisition error and level of detail on the accuracy of spatial analyses

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