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    Approach for the semi-automatic verification of 3D building models

    Access Status
    Fulltext not available
    Authors
    Helmholz, Petra
    Belton, David
    Moncrieff, Simon
    Date
    2013
    Type
    Conference Paper
    
    Metadata
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    Citation
    Helmholz, P. and Belton, D. and Moncrieff, S. 2013. Approach for the semi-automatic verification of 3D building models, in Heipke, C. and Jacobsen, K. and Rottensteiner, F. and Sörgel, U. (ed), International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, May 21-24 2013, XL-1/W1: pp. 121-126. Hannover, Germany: ISPRS Archives.
    Source Title
    ISPRS Archives – Volume XL-1/W1, 2013
    Source Conference
    ISPRS Hannover Workshop 2013 High-Resolution Earth Imaging for Geospatial Information
    DOI
    10.5194/isprsarchives-XL-1-W1-121-2013
    URI
    http://hdl.handle.net/20.500.11937/15414
    Collection
    • Curtin Research Publications
    Abstract

    In the field of spatial sciences, there are a large number of disciplines and techniques for capturing data to solve a variety of different tasks and problems for different applications. Examples include: traditional survey for boundary definitions, aerial imagery for building models, and laser scanning for heritage facades. These techniques have different attributes such as the number of dimensions, accuracy and precision, and the format of the data. However, because of the number of applications and jobs, often over time these data sets captured from different sensor platforms and for different purposes will overlap in some way. In most cases, while this data is archived, it is not used in future applications to value add to the data capture campaign of current projects. It is also the case that newly acquire data are often not used to combine and improve existing models and data integrity. The purpose of this paper is to discuss a methodology and infrastructure to automatically support this concept. That is, based on a job specification, to automatically query existing and newly acquired data based on temporal and spatial relations, and to automatically combine and generate the best solution. To this end, there are three main challenges to examine; change detection, thematic accuracy and data matching.

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