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首页> 外文会议>AREMA annual conference >UAVs: Beyond Pretty Pictures A Case Study in Analyzing the Integrity of UAV-Derived Photogrammetry Data for Full-Scale Railroad Infrastructure Design
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UAVs: Beyond Pretty Pictures A Case Study in Analyzing the Integrity of UAV-Derived Photogrammetry Data for Full-Scale Railroad Infrastructure Design

机译：无人机：除了漂亮的图画之外，还进行了一个案例研究，该案例分析了无人机的摄影测量数据的完整性，以进行全面的铁路基础设施设计

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UAV-derived photogrammetry is a relatively new technological surveying advancement. Photogrammetry is where imagery taken by UAVs (Unmanned Aerial Vehicles) is processed by computer software to generate dense topographical data networks representing where the aerial images were taken. A team at Norfolk Southern Railway used photogrammetry to investigate whether the resulting data is accurate enough to replace traditional ground surveying methods for use on complete infrastructure expansion projects. The goal was to determine if photogrammetric results would be useful, given the complexities that are common on large scale linear infrastructure projects. The team analyzed the UAV-derived photogrammetry data and found a 0.10 foot average elevation difference in top of rail elevation versus traditional surveying methods, a 0.20 foot average elevation difference within 25 feet of the rail, a 0.30 foot average elevation difference in gently rolling terrain with general no tree canopy, and a 6.80 foot average elevation difference in areas with a dense tree canopy and steep terrain. Based on these results, the photogrammetry data for top of rail and terrain within 25 feet of the rail was within or near standard tolerances and could be used for design. Areas sampled with dense canopies or dense undergrowth did not provide photogrammetry data within standard tolerances, and thus would not be usable for a complete design. While not a perfect or complete solution, the team found that UAV-derived photogrammetry can provide design-grade data for complete civil designs on certain applications. We found it saves time, minimizes initial capital costs, and almost completely avoids conflict with railroad operations. With proper flight planning, an understanding of the appropriate terrain applications, and future advancements in the photogrammetry machine learning algorithms, the team is confident in the use of photogrammetry software to generate design-grade data for construction plans and construction quantity calculations on large scale railroad infrastructure projects. In the end, UAVs may very well be more than just a way to take impressive videos and pictures.
机译：无人机衍生的摄影测量是一种相对较新的技术测量进步。摄影测量是由UAVS（无人机航空车辆）拍摄的图像（无人驾驶航空车辆）的图像由计算机软件处理，以生成代表在拍摄航空图像的位置的密集地形数据网络。诺福克南部铁路的团队使用摄影测量来调查所得数据是否足以取代传统的地面测量方法，以便在完整的基础设施扩展项目上使用。鉴于大规模线性基础设施项目中常见的复杂性，目标是确定摄影测量结果是否有用。该团队分析了无人机衍生的摄影测量数据，并发现了轨道高程顶部的0.10英尺平均海拔差异与传统测量方法，在轨道25英尺范围内的0.20英尺平均海拔差异，平均水平升高的平均升高滚动地形一般没有树木篷，以及一个6.80英尺的平均海拔差异，浓密的树木冠层和陡峭的地形。基于这些结果，铁路25英尺范围内的导轨和地形顶部的摄影测量数据在标准公差范围内或附近，可用于设计。采样与密集的檐篷或密集的底层采样的区域没有在标准公差中提供摄影测量数据，因此不可用于完整的设计。虽然不是完美或完整的解决方案，但该团队发现无人机衍生的摄影测量测量可以为某些应用提供完整的民用设计的设计级数据。我们发现它节省了时间，最大限度地减少了初始资本成本，并且几乎完全避免了与铁路操作的冲突。通过适当的飞行计划，对摄影测量机器学习算法中的适当地形应用以及未来的进步，该团队对使用摄影测量软件充满信心，为大规模铁路的施工计划和施工数量计算产生设计级数据基建项目。到底，无人机可能比以令人印象深刻的视频和图片更多的方式。

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