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Building-Level Change Detection from Large-Scale Historical Vector Data by Using Direct and a Three-Tier Post-classification Comparison

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

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Abstract

Historical change information at the building level in urban areas is crucial for policy and resource management, especially in countries with densely population and quick building construction. In this paper we present a multi-level building change detection framework using large-scale historical vector and address-based data. This approach is fundamentally different to the traditionally ones which purely use remotely sensed images and are often limited in identifying functional characteristics. Ordnance Survey’s (OS) MasterMap in the UK has been taken as an example of the large-scale vector data. The buildings features are extracted for two years and are compared to identify modified, demolished, and un-changed ones. To quantify buildings’ functional changes, an earlier developed classification methodology was used by extracting cartomteric and spatial properties of buildings and linking contextual information from address-based data. The case study in Manchester, UK shows that the proposed approach can successfully identify building changes at multiple levels. The change detection framework presented here closely addresses how to use large-scale and existing data sources to create a historical land use database. Moreover, this framework is computationally robust and is applicable to other areas without losing its integrity within and outside the UK, where large-scale structured data sets are available.

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Notes

  1. 1.

    Ordnance Survey introduced several rules to identify whether the real-world object after modification is the same or a new one. These rules are given in the user guide for Topographic layer. The URL as per October 2014 is: http://www.ordnancesurvey.co.uk/docs/user-guides/os-mastermap-topography-layer-user-guide.pdf.

  2. 2.

    Cartometry is a branch of science related to measuring various attributes of objects from a map. The measurements which are made from maps as also called ‘cartometric quantities’ [39, 40].

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Authors and Affiliations

  1. Department of Geography and Planning, Queen’s University, Kingston, ON, K7L 3N6, Canada

    Masroor Hussain & Dongmei Chen

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  1. Masroor Hussain
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  2. Dongmei Chen
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Correspondence to Dongmei Chen .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

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Hussain, M., Chen, D. (2018). Building-Level Change Detection from Large-Scale Historical Vector Data by Using Direct and a Three-Tier Post-classification Comparison. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10962. Springer, Cham. https://doi.org/10.1007/978-3-319-95168-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-95168-3_20

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