Please use this identifier to cite or link to this item: https://doi.org/10.5194/isprs-annals-IV-4-W1-51-2016
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dc.titleGENERATION of MULTI-LOD 3D CITY MODELS in CITYGML with the PROCEDURAL MODELLING ENGINE RANDOM3DCITY
dc.contributor.authorBiljecki F.
dc.contributor.authorLedoux H.
dc.contributor.authorStoter J.
dc.date.accessioned2018-10-05T08:04:46Z
dc.date.available2018-10-05T08:04:46Z
dc.date.issued2016
dc.identifier.citationBiljecki F., Ledoux H., Stoter J. (2016). GENERATION of MULTI-LOD 3D CITY MODELS in CITYGML with the PROCEDURAL MODELLING ENGINE RANDOM3DCITY. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4 (4W1) : 51-59. ScholarBank@NUS Repository. https://doi.org/10.5194/isprs-annals-IV-4-W1-51-2016
dc.identifier.issn21949042
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/148025
dc.description.abstractThe production and dissemination of semantic 3D city models is rapidly increasing benefiting a growing number of use cases. However, their availability in multiple LODs and in the CityGML format is still problematic in practice. This hinders applications and experiments where multi-LOD datasets are required as input, for instance, to determine the performance of different LODs in a spatial analysis. An alternative approach to obtain 3D city models is to generate them with procedural modelling, which is-as we discuss in this paper-well suited as a method to source multi-LOD datasets useful for a number of applications. However, procedural modelling has not yet been employed for this purpose. Therefore, we have developed RANDOM3DCITY, an experimental procedural modelling engine for generating synthetic datasets of buildings and other urban features. The engine is designed to produce models in CityGML and does so in multiple LODs. Besides the generation of multiple geometric LODs, we implement the realisation of multiple levels of spatiosemantic coherence, geometric reference variants, and indoor representations. As a result of their permutations, each building can be generated in 392 different CityGML representations, an unprecedented number of modelling variants of the same feature. The datasets produced by RANDOM3DCITY are suited for several applications, as we show in this paper with documented uses. The developed engine is available under an open-source licence at Github at <a hrefCombining double low line"http://github.com/tudelft3d/Random3Dcity"targetCombining double low line"-blank">http://github.com/tudelft3d/Random3Dcity</a>.
dc.publisherCopernicus GmbH
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentDEPT OF ARCHITECTURE
dc.contributor.departmentREAL ESTATE
dc.description.doi10.5194/isprs-annals-IV-4-W1-51-2016
dc.description.sourcetitleISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
dc.description.volume4
dc.description.issue4W1
dc.description.page51-59
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