Feature recognition and clustering for urban modelling: Exploration and analysis in GIS and CAD

Jose Beirao, Andre Chaszar

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientific


    In urban planning exploration and analysis assist the generation,
    measurement, interpretation and management of the modelled urban environments.
    This frequently involves categorisation of model elements and identification of element types. Such designation of elements can be achieved through attribution (e.g. ‘tagging’ or ‘layering’) or direct selection by model users. However, for large, complex models the number and arrangement of elements makes these approaches impractical in terms of time/effort and accuracy. This is particularly true of models which include substantial numbers of elements representing existing urban fabric, rather than only newly generated elements (which might be automatically attributed during the generation process). We present methods for identification and categorisation of model elements in models of existing and proposed urban agglomerations.
    We also suggest how these methods can enable exploration of models, discovery
    of identities and relationships not otherwise obvious, and acquisition of insights to the models’ structure and contents which are not captured, and may even be obscured, by manual selection or automated pre-attribution.
    Original languageEnglish
    Title of host publicationOpen Systems
    Subtitle of host publicationProceedings of the 18th International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2013)
    EditorsR. Stouffs, P. Janssen, S. Roudavski, B. Tuncer
    Place of PublicationHong Kong
    PublisherThe Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)
    Publication statusPublished - 2013

    Fingerprint Dive into the research topics of 'Feature recognition and clustering for urban modelling: Exploration and analysis in GIS and CAD'. Together they form a unique fingerprint.

    Cite this