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  • A Novel Building Change Detection Method Using 3D Building Models

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    SiddiquiPUB2715.pdf (455.7Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Siddiqui, Fasahat Ullah
    Awrangjeb, Mohammad
    Griffith University Author(s)
    Awrangjeb, Mohammad
    Year published
    2017
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    Abstract
    The traditional procedure for the building change detection is subjective to user's knowledge of the involved data. The complexity increases further due to unavailability of a public data set that is scanned on two different dates. Therefore, the manual changes in the reference data are more common to generate modified data. This paper first presents a strategy to introduce five types of changes in the reference data. Next, the proposed robust building change detection method runs analysis on planes' connectivity to represent the 3D building models of the reference and modified data. Based on the plane connection information, ...
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    The traditional procedure for the building change detection is subjective to user's knowledge of the involved data. The complexity increases further due to unavailability of a public data set that is scanned on two different dates. Therefore, the manual changes in the reference data are more common to generate modified data. This paper first presents a strategy to introduce five types of changes in the reference data. Next, the proposed robust building change detection method runs analysis on planes' connectivity to represent the 3D building models of the reference and modified data. Based on the plane connection information, the 3D building models are compared for identification of the building changes and classify them into unchanged, demolished, new, modified, and partially-modified planes. The experimental results show that the proposed building change detection method successfully detects all introduced changes in the test data set.
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    Conference Title
    2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA)
    Volume
    2017-December
    DOI
    https://doi.org/10.1109/DICTA.2017.8227394
    Copyright Statement
    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Artificial intelligence
    Photogrammetry and remote sensing
    Publication URI
    http://hdl.handle.net/10072/368377
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    • Conference outputs

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