IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i8p1522-d1207389.html
   My bibliography  Save this article

SE-YOLOv7 Landslide Detection Algorithm Based on Attention Mechanism and Improved Loss Function

Author

Listed:
  • Qing Liu

    (School of Mechanical and Electrical Engineering, Huainan Normal University, Huainan 232038, China)

  • Tingting Wu

    (Shaanxi Key Laboratory of Land Consolidation, Chang’an University, Xi’an 710054, China
    School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China)

  • Yahong Deng

    (School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China)

  • Zhiheng Liu

    (School of Aerospace Science and Technology, Xidian University, Xi’an 710054, China)

Abstract

With the continuous development of computer vision technology, more and more landslide identification detection tasks have started to shift from manual visual interpretation to automatic computer identification, and automatic landslide detection methods based on remote sensing satellite images and deep learning have been gradually developed. However, most existing algorithms often have the problem of low precision and weak generalization in landslide detection. Based on the Google Earth Engine platform, this study selected landslide image data from 24 study areas in China and established the DN landslide sample dataset, which contains a total of 1440 landslide samples. The original YOLOv7 algorithm model was improved and optimized by applying the SE squeezed attention mechanism and VariFocal loss function to construct the SE-YOLOv7 model to realize the automatic detection of landslides in remote sensing images. The experimental results show that the mAP, Precision value, Recall value, and F1-Score of the improved SE-YOLOv7 model for landslide identification are 91.15%, 93.35%, 94.54%, and 93.94%, respectively. At the same time, through a field investigation and verification study in Qianyang County, Baoji City, Shaanxi Province, comparing the detection results of SE-YOLOv7, it is concluded that the improved SE-YOLOv7 can locate the landslide location more accurately, detect the landslide range more accurately, and have fewer missed detections. The research results show that the algorithm model has strong detection accuracy for many types of landslide image data, which provides a technical reference for future research on landslide detection based on remote sensing images.

Suggested Citation

  • Qing Liu & Tingting Wu & Yahong Deng & Zhiheng Liu, 2023. "SE-YOLOv7 Landslide Detection Algorithm Based on Attention Mechanism and Improved Loss Function," Land, MDPI, vol. 12(8), pages 1-19, July.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:8:p:1522-:d:1207389
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/8/1522/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/8/1522/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Faraz S. Tehrani & Michele Calvello & Zhongqiang Liu & Limin Zhang & Suzanne Lacasse, 2022. "Machine learning and landslide studies: recent advances and applications," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 1197-1245, November.
    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. Batmyagmar Dashbold & L. Sebastian Bryson & Matthew M. Crawford, 2023. "Landslide hazard and susceptibility maps derived from satellite and remote sensing data using limit equilibrium analysis and machine learning model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 235-265, March.
    2. Esteban Bravo-López & Tomás Fernández Del Castillo & Chester Sellers & Jorge Delgado-García, 2023. "Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods," Land, MDPI, vol. 12(6), pages 1-28, May.
    3. Shengjie Rui & Zhen Guo & Wenjie Zhou, 2023. "Promoting Sustainable Marine Development: Geotechnical Engineering Problems and Environmental Guarantee Technology in Marine Space, Energy, and Resource Development," Sustainability, MDPI, vol. 15(19), pages 1-3, October.
    4. Prahlada V. Mittal & Rishabh Bafna & Ankush Mittal, 2023. "Unsupervised learning framework for region-based damage assessment on xBD, a large satellite imagery," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(2), pages 1619-1643, September.
    5. Lu Fang & Qian Wang & Jianping Yue & Yin Xing, 2023. "Analysis of Optimal Buffer Distance for Linear Hazard Factors in Landslide Susceptibility Prediction," Sustainability, MDPI, vol. 15(13), pages 1-17, June.
    6. Gongfa Chen & Wei Deng & Mansheng Lin & Jianbin Lv, 2023. "Slope stability analysis based on convolutional neural network and digital twin," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(2), pages 1427-1443, September.
    7. Han Zhang & Chao Yin & Shaoping Wang & Bing Guo, 2023. "Landslide susceptibility mapping based on landslide classification and improved convolutional neural networks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(2), pages 1931-1971, March.

    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:8:p:1522-:d:1207389. 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.