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<p>This study presents a comparative evaluation of Unmanned Aerial Vehicle (UAV)-based volumetric analysis against conventional Total Station methods over a 21.24-hectare site featuring 29 potholes. UAV imagery was processed to generate Digital Surface Models (DSMs) and orthomosaics, which were then analysed to estimate cut, fill, and net volumes. The UAV-derived results were benchmarked against those obtained from traditional ground-based surveys. Deviations between the two methods remained within a 3–6% range, with Root Mean Square Error (RMSE) values of 3.8% for cut volume, 5.1% for fill volume, and 4.6% for net volume. Time efficiency analysis revealed that UAV surveys, completed in 3–4&#xa0;h with an additional 14–16&#xa0;h of processing, achieved an approximate 93.8% reduction in total survey duration compared to the 15–16 days required by conventional methods. The study also investigates factors influencing data discrepancies, including terrain complexity, ground control point (GCP) distribution, and shadow interference. Statistical validation using paired t-tests confirmed significant differences between the methods, although the UAV results remained within acceptable tolerance limits for engineering applications. The findings support the viability of UAV-based workflows for high-resolution volumetric assessments in construction, infrastructure planning, and land management, while highlighting the importance of error mitigation strategies in complex terrains.</p>

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From sky to surface: engineering-scale comparison of UAV-based and traditional surveying techniques for volumetric assessment

  • 期刊论文

  • 发表日期：

    2025年10月25日

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  • Aditya Agrawal,
  • Manojkumar Deshpande,
  • Lalit Nagapurkar,
  • Abhishek Chourasiya

摘要

This study presents a comparative evaluation of Unmanned Aerial Vehicle (UAV)-based volumetric analysis against conventional Total Station methods over a 21.24-hectare site featuring 29 potholes. UAV imagery was processed to generate Digital Surface Models (DSMs) and orthomosaics, which were then analysed to estimate cut, fill, and net volumes. The UAV-derived results were benchmarked against those obtained from traditional ground-based surveys. Deviations between the two methods remained within a 3–6% range, with Root Mean Square Error (RMSE) values of 3.8% for cut volume, 5.1% for fill volume, and 4.6% for net volume. Time efficiency analysis revealed that UAV surveys, completed in 3–4 h with an additional 14–16 h of processing, achieved an approximate 93.8% reduction in total survey duration compared to the 15–16 days required by conventional methods. The study also investigates factors influencing data discrepancies, including terrain complexity, ground control point (GCP) distribution, and shadow interference. Statistical validation using paired t-tests confirmed significant differences between the methods, although the UAV results remained within acceptable tolerance limits for engineering applications. The findings support the viability of UAV-based workflows for high-resolution volumetric assessments in construction, infrastructure planning, and land management, while highlighting the importance of error mitigation strategies in complex terrains.