• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • A Framework of Linear Sensor Networks with Unmanned Aerial Vehicle for Rainfall-Induced Landslides Detection

    Author(s)
    Yang, B
    Qiu, Q
    Yang, F
    Guan, H
    Griffith University Author(s)
    Guan, Hong
    Yang, Fuwen
    Qiu, Quanwei
    Year published
    2020
    Metadata
    Show full item record
    Abstract
    This paper proposes a real-time monitoring framework for a landslide susceptibility area based on wireless sensor network using multiple Unmanned Aerial Vehicles (UAVs). Many researchers have considered building a landslide susceptibility map to distinguish different levels of landslide susceptible zones. However, to prevent damage from landslides, it is more important for the disaster control center to identify the time and location of the landslide occurrence in those highly susceptible areas. Hence, a rain-triggered landslide monitoring system is proposed herein for local mountain areas. First, a wireless sensor network ...
    View more >
    This paper proposes a real-time monitoring framework for a landslide susceptibility area based on wireless sensor network using multiple Unmanned Aerial Vehicles (UAVs). Many researchers have considered building a landslide susceptibility map to distinguish different levels of landslide susceptible zones. However, to prevent damage from landslides, it is more important for the disaster control center to identify the time and location of the landslide occurrence in those highly susceptible areas. Hence, a rain-triggered landslide monitoring system is proposed herein for local mountain areas. First, a wireless sensor network framework is constructed to inform the control center as immediately as possible when landslides occur. Second, multiple UAV sensors will be responsible for collecting the stereo images of the slope in highly sensitive zones on schedule. Based on the stereo images and the binocular model, in-depth information can be obtained. With the depth information and Speeded Up Robust Features (SURF) detection, the key point characteristic information is constructed as the input data for Support Vector Machine (SVM). An SVM algorithm is designed with Python program language and executed in real time. Using this algorithm, the real-time images collected by UAVs and the landslide warning information will be sent to the control center for further analysis. Finally, a field experiment is conducted to demonstrate the effectiveness of the proposed method.
    View less >
    Journal Title
    International Journal of Structural Stability and Dynamics
    Volume
    20
    Issue
    10
    DOI
    https://doi.org/10.1142/S0219455420420171
    Subject
    Civil Engineering
    Mechanical Engineering
    Publication URI
    http://hdl.handle.net/10072/400472
    Collection
    • Journal articles

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander