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<p>Data quality and spatial resolution play an important role in the study of debris flow morphology and its evolution, contributing to the characterization of the related hazard. Satellites imagery exhibits substantial errors in mountainous regions with pronounced elevation variability. Formerly developed backpack-type advanced channel detection and mapping system (AscDAMs) could fulfill this requirement. However, owing to manpower constraints, the efficiency and coverage of AscDAMs remain limited. Although unmanned aerial vehicles (UAV) with autonomous exploration capability can be expected to overcome these limitations, the existing algorithms are not suitable for the semi-enclosed environment such as debris flow gullies. To allow UAV-based autonomous data acquisition inside debris flow channel, this study proposes a novel auto-exploration system, named AscDAMs 2.0, by transitioning the deployment of sensors from backpack-type platform to UAV. The new algorithm includes a point cloud merger, a height estimator, and an optimal direction calculator, which enable the UAV to automatically navigate and map the complex mountainous debris flow channel. AscDAMs 2.0 was successfully tested in Chutou Gully and Banzi Gully in Wenchuan County (China) achieving a spatial resolution of 0.1&#xa0;m for mapping debris flow channel morphology. Compared to backpack-type mapping systems, AscDAMs 2.0 enables remote-controlled and faster surveys, acquiring more data while minimizing operational risks during data acquisition in hazardous environments. AscDAMs 2.0 demonstrates the potential for frequent, regular detection of debris flow channels, offering applications in risk assessment, hazard mitigation, and disaster forecasting and early warning.</p>

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AscDAMs 2.0: advanced SLAM-and-UAV-based channel detection and mapping system

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月22日

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  • Fucheng Lu,
  • Tengfei Wang,
  • Hui Kong,
  • Jianyang Zhu,
  • Ping Shen

摘要

Data quality and spatial resolution play an important role in the study of debris flow morphology and its evolution, contributing to the characterization of the related hazard. Satellites imagery exhibits substantial errors in mountainous regions with pronounced elevation variability. Formerly developed backpack-type advanced channel detection and mapping system (AscDAMs) could fulfill this requirement. However, owing to manpower constraints, the efficiency and coverage of AscDAMs remain limited. Although unmanned aerial vehicles (UAV) with autonomous exploration capability can be expected to overcome these limitations, the existing algorithms are not suitable for the semi-enclosed environment such as debris flow gullies. To allow UAV-based autonomous data acquisition inside debris flow channel, this study proposes a novel auto-exploration system, named AscDAMs 2.0, by transitioning the deployment of sensors from backpack-type platform to UAV. The new algorithm includes a point cloud merger, a height estimator, and an optimal direction calculator, which enable the UAV to automatically navigate and map the complex mountainous debris flow channel. AscDAMs 2.0 was successfully tested in Chutou Gully and Banzi Gully in Wenchuan County (China) achieving a spatial resolution of 0.1 m for mapping debris flow channel morphology. Compared to backpack-type mapping systems, AscDAMs 2.0 enables remote-controlled and faster surveys, acquiring more data while minimizing operational risks during data acquisition in hazardous environments. AscDAMs 2.0 demonstrates the potential for frequent, regular detection of debris flow channels, offering applications in risk assessment, hazard mitigation, and disaster forecasting and early warning.