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Building Extraction from Unmanned Aerial Vehicle (UAV) Data in a Landslide-Affected Scattered Mountainous Area Based on Res-Unet

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  • Chunhai Tan

    (Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
    Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Tao Chen

    (Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
    Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Jiayu Liu

    (Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
    Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Xin Deng

    (Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
    Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Hongfei Wang

    (Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
    Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China)

  • Junwei Ma

    (Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China
    Three Gorges Research Center for Geo-Hazards of the Ministry of Education, China University of Geosciences, Wuhan 430074, China
    Hubei Key Laboratory of Operation Safety of High Dam and Large Reservoir, Yichang 431000, China)

Abstract

Building extraction in landslide-affected scattered mountainous areas is essential for sustainable development, as it improves disaster risk management, fosters sustainable land use, safeguards the environment, and bolsters socio-economic advancement; however, this process entails considerable challenges. This study proposes a Res-Unet-based model to extract landslide-affected buildings from unmanned aerial vehicle (UAV) data in scattered mountain regions, leveraging the feature extraction capabilities of ResNet and the precise localization abilities of U-Net. A landslide-affected, scattered mountainous region within the Three Gorges Reservoir area was selected as a case study to validate the model’s performance. Experimental results indicate that Res-Unet displays high accuracy and robustness in building recognition, attaining accuracy (ACC), intersection-over-union (IOU), and F1-score values of 0.9849, 0.9785, and 0.9892, respectively. This enhancement can be attributed to the combined model, which amalgamates the skip connections, the symmetric architecture of U-Net, and the residual blocks of ResNet. This integration preserves low-level detail during recovery at higher levels, facilitating the extraction of multi-scale features while also mitigating the vanishing gradient problem prevalent in deep network training through the residual block structure, thus enabling the extraction of more complex features. The proposed Res-Unet approach shows significant potential for the accurate recognition and extraction of buildings in complex terrains through the efficient processing of remote sensing images.

Suggested Citation

  • Chunhai Tan & Tao Chen & Jiayu Liu & Xin Deng & Hongfei Wang & Junwei Ma, 2024. "Building Extraction from Unmanned Aerial Vehicle (UAV) Data in a Landslide-Affected Scattered Mountainous Area Based on Res-Unet," Sustainability, MDPI, vol. 16(22), pages 1-15, November.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9791-:d:1517624
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    References listed on IDEAS

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    1. Lin Luo & Pengpeng Li & Xuesong Yan, 2021. "Deep Learning-Based Building Extraction from Remote Sensing Images: A Comprehensive Review," Energies, MDPI, vol. 14(23), pages 1-25, November.
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