IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p2936-d1059491.html
   My bibliography  Save this article

Proposal of a Disrupted Road Detection Method in a Tsunami Event Using Deep Learning and Spatial Data

Author

Listed:
  • Jun Sakamoto

    (Faculty of Science and Technology, Kochi University, Kochi 780-8520, Japan)

Abstract

Tsunamis generated by undersea earthquakes can cause severe damage. It is essential to quickly assess tsunami-damaged areas to take emergency measures. In this study, I employ deep learning and develop a model using aerial photographs and road segment data. I obtained data from the aerial photographs taken after the Great East Japan Earthquake; the deep learning model used was YOLOv5. The proposed method based on YOLOv5 can determine damaged roads from aerial pictures taken after a disaster. The feature of the proposed method is to use training data from images separated by a specific range and to distinguish the presence or absence of damage related to the tsunami. The results show that the proposed method is more accurate than a comparable traditional method, which is constructed by labeling and learning the damaged areas. The highest F1 score of the traditional method was 60~78%, while the highest F1 score of the proposed method was 72~83%. The traditional method could not detect locations where it is difficult to determine the damage status from aerial photographs, such as where houses are not completely damaged. However, the proposed method was able to detect them.

Suggested Citation

  • Jun Sakamoto, 2023. "Proposal of a Disrupted Road Detection Method in a Tsunami Event Using Deep Learning and Spatial Data," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:2936-:d:1059491
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/2936/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/4/2936/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Francesca Raffini & Giorgio Bertorelle & Roberto Biello & Guido D’Urso & Danilo Russo & Luciano Bosso, 2020. "From Nucleotides to Satellite Imagery: Approaches to Identify and Manage the Invasive Pathogen Xylella fastidiosa and Its Insect Vectors in Europe," Sustainability, MDPI, vol. 12(11), pages 1-38, 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. Zhenan Jin & Wentao Yu & Haoxiang Zhao & Xiaoqing Xian & Kaiting Jing & Nianwan Yang & Xinmin Lu & Wanxue Liu, 2022. "Potential Global Distribution of Invasive Alien Species, Anthonomus grandis Boheman, under Current and Future Climate Using Optimal MaxEnt Model," Agriculture, MDPI, vol. 12(11), pages 1-14, October.
    2. C. Emdad Haque & Parnali Dhar-Chowdhury & Shakhawat Hossain & David Walker, 2023. "Spatial Evaluation of Dengue Transmission and Vector Abundance in the City of Dhaka, Bangladesh," Geographies, MDPI, vol. 3(2), pages 1-18, April.
    3. Zhengxin Ji & Hejie Wei & Dong Xue & Mengxue Liu & Enxiang Cai & Weiqiang Chen & Xinwei Feng & Jiwei Li & Jie Lu & Yulong Guo, 2021. "Trade-Off and Projecting Effects of Land Use Change on Ecosystem Services under Different Policies Scenarios: A Case Study in Central China," IJERPH, MDPI, vol. 18(7), pages 1-23, March.
    4. Antonio J. Mendoza-Fernández & Fabián Martínez-Hernández & Esteban Salmerón-Sánchez & Francisco J. Pérez-García & Blas Teruel & María E. Merlo & Juan F. Mota, 2020. "The Relict Ecosystem of Maytenus senegalensis subsp. europaea in an Agricultural Landscape: Past, Present and Future Scenarios," Land, MDPI, vol. 10(1), pages 1-15, December.
    5. Francesco Bozzo & Michel Frem & Vincenzo Fucilli & Gianluigi Cardone & Paolo Francesco Garofoli & Stefania Geronimo & Alessandro Petrontino, 2022. "Landscape and Vegetation Patterns Zoning Is a Methodological Tool for Management Costs Implications Due to Xylella fastidiosa Invasion," Land, MDPI, vol. 11(7), pages 1-19, July.
    6. Kaoutar El Handi & Majida Hafidi & Khaoula Habbadi & Maroun El Moujabber & Mohamed Ouzine & Abdellatif Benbouazza & Miloud Sabri & El Hassan Achbani, 2021. "Assessment of Ionomic, Phenolic and Flavonoid Compounds for a Sustainable Management of Xylella fastidiosa in Morocco," Sustainability, MDPI, vol. 13(14), pages 1-11, July.

    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:jsusta:v:15:y:2023:i:4:p:2936-:d:1059491. 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.