IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i2p277-d1040282.html
   My bibliography  Save this article

Identification of Facade Elements of Traditional Areas in Seoul, South Korea

Author

Listed:
  • Donghwa Shon

    (Department of Architecture, Chungbuk National University, Cheongju 28644, Republic of Korea)

  • Giyoung Byun

    (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea)

  • Soyoung Choi

    (Institute of Construction and Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea)

Abstract

The Bukchon area in Seoul boasts a high density of Hanok, the traditional Korean architecture representing the region. Because the Hanok facade plays a vital role in the streetscape formation, we must record it in terms of social, cultural, historical, artistic, and scenic values. However, recording the facade of an existing Hanok building through drawing or image information is time consuming and labor intensive, and therefore costly. Further, its digital conversion is inherently difficult. This study proposes the use of deep learning to identify the form elements that comprise the Hanok facade. Three-dimensional modeling was performed on 405 well-preserved Hanok facades in the region, and 2808 items of image data were created under similar conditions and at differing angles. Labeling was performed on the shape elements of the Hanok facade, and a methodology was established to identify the facade elements using MASK R-CNN. The type of roof, windows, the lower part of the outer wall, and the design were identified with high accuracy.

Suggested Citation

  • Donghwa Shon & Giyoung Byun & Soyoung Choi, 2023. "Identification of Facade Elements of Traditional Areas in Seoul, South Korea," Land, MDPI, vol. 12(2), pages 1-22, January.
  • Handle: RePEc:gam:jlands:v:12:y:2023:i:2:p:277-:d:1040282
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/2/277/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/12/2/277/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Seung-Yeul Ji & Han-Jong Jun, 2020. "Deep Learning Model for Form Recognition and Structural Member Classification of East Asian Traditional Buildings," Sustainability, MDPI, vol. 12(13), pages 1-18, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hail Jung & Jeongjin Rhee, 2022. "Application of YOLO and ResNet in Heat Staking Process Inspection," Sustainability, MDPI, vol. 14(23), pages 1-14, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:12:y:2023:i:2:p:277-:d:1040282. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.