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Assessing a Semi-Autonomous Drone-in-a-Box System for Landslide Monitoring: A Case Study from the Yukon Territory, Canada

Author

Listed:
  • Margaret Kalacska

    (Applied Remote Sensing Lab, Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada)

  • Oliver Lucanus

    (Applied Remote Sensing Lab, Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada)

  • Juan Pablo Arroyo-Mora

    (Aerospace Research Centre, Flight Research Laboratory, National Research Council of Canada, Ottawa, ON K1V 2B1, Canada
    Earth and Planetary Sciences Department, McGill University, Montreal, QC H3A 0E8, Canada)

  • John Stix

    (Earth and Planetary Sciences Department, McGill University, Montreal, QC H3A 0E8, Canada)

  • Panya Lipovsky

    (Yukon Geological Survey, P.O. Box 2703 (K-14), Whitehorse, YT Y1A 2C6, Canada)

  • Justin Roman

    (Earth and Planetary Sciences Department, McGill University, Montreal, QC H3A 0E8, Canada)

Abstract

Technological innovation in commercial Remotely Piloted Aircraft Systems (RPASs) is advancing rapidly. However, their operational efficiency remains limited by the need for on-site skilled human operators. Semi-autonomous drone-in-a-box (DIAB) systems are emerging as a practical solution, enabling automated, repeatable missions for applications such as construction site monitoring, security, and critical infrastructure inspection. Beyond industry, these systems hold significant promise for scientific research, particularly in long-term environmental monitoring where cost, accessibility, and safety are critical factors. In this technology demonstration, we detail the system implementation, discuss flight-planning challenges, and assess the overall feasibility of deploying a DJI Dock 2 DIAB system for remote monitoring of the Miles Ridge landslide in the Yukon Territory, Canada. The system was installed approximately 2.5 km from the landslide and operated remotely from across the country in Montreal, QC, about 4000 km away. A total of five datasets were acquired from July to September 2025, enabling three-dimensional reconstruction of the landslide surface from each acquisition. A comparison of extracted cross-sections demonstrated high reproducibility and accurate co-registration across acquisitions. This study highlights the potential of DIAB systems to support reliable, low-maintenance monitoring of remote landslides.

Suggested Citation

  • Margaret Kalacska & Oliver Lucanus & Juan Pablo Arroyo-Mora & John Stix & Panya Lipovsky & Justin Roman, 2026. "Assessing a Semi-Autonomous Drone-in-a-Box System for Landslide Monitoring: A Case Study from the Yukon Territory, Canada," Sustainability, MDPI, vol. 18(2), pages 1-21, January.
  • Handle: RePEc:gam:jsusta:v:18:y:2026:i:2:p:693-:d:1837118
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