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Artificial Intelligence for the Detection of Asbestos Cement Roofing: An Investigation of Multi-Spectral Satellite Imagery and High-Resolution Aerial Imagery

Author

Listed:
  • Mia V. Hikuwai

    (Griffith School of Engineering and Built Environment, Griffith University, Parklands Drive, Southport, QLD 4222, Australia
    UACS Consulting Pty Ltd., 12/102 Burnett Street, Buderim, QLD 4556, Australia)

  • Nicholas Patorniti

    (UACS Consulting Pty Ltd., 12/102 Burnett Street, Buderim, QLD 4556, Australia
    Cities Research Institute, Griffith University, Parklands Drive, Southport, QLD 4222, Australia)

  • Abel S. Vieira

    (Griffith School of Engineering and Built Environment, Griffith University, Parklands Drive, Southport, QLD 4222, Australia
    UACS Consulting Pty Ltd., 12/102 Burnett Street, Buderim, QLD 4556, Australia)

  • Georgia Frangioudakis Khatib

    (Australian Government Asbestos Safety and Eradication Agency, Surry Hills, NSW 2010, Australia)

  • Rodney A. Stewart

    (Griffith School of Engineering and Built Environment, Griffith University, Parklands Drive, Southport, QLD 4222, Australia
    Cities Research Institute, Griffith University, Parklands Drive, Southport, QLD 4222, Australia)

Abstract

Artificial Intelligence (AI) is providing the technology for large-scale, cost-effective and current asbestos-containing material (ACM) roofing detection. AI models can provide additional data to monitor, manage and plan for ACM in situ and its safe removal and disposal, compared with traditional approaches alone. Advances are being made in AI algorithms and imagery applied to ACM detection. This study applies mask region-based convolution neural networks (Mask R-CNN) to multi-spectral satellite imagery (MSSI) and high-resolution aerial imagery (HRAI) to detect the presence of ACM roofing on residential buildings across an Australian case study area. The results provide insights into the challenges and benefits of using AI and different imageries for ACM detection, providing future directions for its practical application. The study found model 1, using HRAI and 460 training samples, was the more reliable model of the three with a precision of 94%. These findings confirm the efficacy of combining advanced AI techniques and remote sensing imagery, specifically Mask R-CNN with HRAI, for ACM roofing detection. Such combinations can provide efficient methods for the large-scale detection of ACM roofing, improving the coverage and currency of data for the implementation of coordinated management policies for ACM in the built environment.

Suggested Citation

  • Mia V. Hikuwai & Nicholas Patorniti & Abel S. Vieira & Georgia Frangioudakis Khatib & Rodney A. Stewart, 2023. "Artificial Intelligence for the Detection of Asbestos Cement Roofing: An Investigation of Multi-Spectral Satellite Imagery and High-Resolution Aerial Imagery," Sustainability, MDPI, vol. 15(5), pages 1-23, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4276-:d:1082625
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    References listed on IDEAS

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    1. Sugio Furuya & Odgerel Chimed-Ochir & Ken Takahashi & Annette David & Jukka Takala, 2018. "Global Asbestos Disaster," IJERPH, MDPI, vol. 15(5), pages 1-11, May.
    2. Mohammad Abbasi & Sherif Mostafa & Abel Silva Vieira & Nicholas Patorniti & Rodney A. Stewart, 2022. "Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review," Sustainability, MDPI, vol. 14(13), pages 1-29, July.
    3. Belinda Brown & Ian Hollins & Joe Pickin & Sally Donovan, 2023. "Asbestos Stocks and Flows Legacy in Australia," Sustainability, MDPI, vol. 15(3), pages 1-9, January.
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