IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v8y2023i5p80-d1135919.html
   My bibliography  Save this article

Remote Sensing Data Preparation for Recognition and Classification of Building Roofs

Author

Listed:
  • Emil Hristov

    (GATE Institute, Sofia University “St. Kliment Ohridski”, 1504 Sofia, Bulgaria)

  • Dessislava Petrova-Antonova

    (GATE Institute, Sofia University “St. Kliment Ohridski”, 1504 Sofia, Bulgaria
    Faculty of Mathematics and Informatics, Sofia University “St. Kliment Ohridski”, 1164 Sofia, Bulgaria)

  • Aleksandar Petrov

    (GATE Institute, Sofia University “St. Kliment Ohridski”, 1504 Sofia, Bulgaria)

  • Milena Borukova

    (GATE Institute, Sofia University “St. Kliment Ohridski”, 1504 Sofia, Bulgaria)

  • Evgeny Shirinyan

    (GATE Institute, Sofia University “St. Kliment Ohridski”, 1504 Sofia, Bulgaria)

Abstract

Buildings are among the most significant urban infrastructure that directly affects citizens’ livelihood. Knowledge about their rooftops is essential not only for implementing different Levels of Detail (LoD) in 3D city models but also for performing urban analyses related to usage potential (solar, green, social), construction assessment, maintenance, etc. At the same time, the more detailed information we have about the urban environment, the more adequate urban digital twins we can create. This paper proposes an approach for dataset preparation using an orthophoto with a resolution of 10 cm. The goal is to obtain roof images into separate GeoTIFFs categorised by type (flat, pitched, complex) in a way suitable for feeding rooftop classification models. Although the dataset is initially elaborated for rooftop classification, it can be applied to developing other deep-learning models related to roof recognition, segmentation, and usage potential estimation. The dataset consists of 3617 roofs covering the Lozenets district of Sofia, Bulgaria. During its preparation, the local-specific context is considered.

Suggested Citation

  • Emil Hristov & Dessislava Petrova-Antonova & Aleksandar Petrov & Milena Borukova & Evgeny Shirinyan, 2023. "Remote Sensing Data Preparation for Recognition and Classification of Building Roofs," Data, MDPI, vol. 8(5), pages 1-19, April.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:5:p:80-:d:1135919
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/8/5/80/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/8/5/80/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jason Pomeroy, 2012. "Room at the Top—The Roof as an Alternative Habitable / Social Space in the Singapore Context," Journal of Urban Design, Taylor & Francis Journals, vol. 17(3), pages 413-424.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Akira A. de Moura Galvão Uematsu & Anarosa A. F. Brandão, 2023. "eMailMe: A Method to Build Datasets of Corporate Emails in Portuguese," Data, MDPI, vol. 8(8), pages 1-12, July.

    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. Ahmed Ehab & Tim Heath, 2023. "Exploring Immersive Co-Design: Comparing Human Interaction in Real and Virtual Elevated Urban Spaces in London," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    2. Yan Li & Hongwu Du & Ceren Sezer, 2022. "Sky Gardens, Public Spaces and Urban Sustainability in Dense Cities: Shenzhen, Hong Kong and Singapore," Sustainability, MDPI, vol. 14(16), pages 1-20, August.

    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:jdataj:v:8:y:2023:i:5:p:80-:d:1135919. 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.