Surface subsidence and its secondary effects caused by underground coal mining continually threaten the safety and property of residents in mining areas. The commonly used total station and leveling approach is time-consuming and laborious, and the spatial range is limited. In this study, an FEIMA D2000 quadrotor unmanned aerial vehicle (UAV) was used to conduct 4-cm / pixel photogrammetry in four trial periods over a surface influence area (3.75 km2) of a working mine face. The performance comparison experiments from five point-cloud filtering algorithms in two sub-regions of the study area show that the ATIN algorithm provides the best filtering for this study area. Coal mining subsidence monitoring in mountainous area based on time-series UAV photogrammetry technology is proposed. Using this approach, the ATIN algorithm was used to filter the overall point cloud data in the study area to obtain the digital elevation model (DEM). The surface dynamic subsidence basin was determined with two phases of DEM subtraction. The results show that, compared with the measured leveling data in the field in the same period, the average root mean square error is 165 mm, and the maximum subsidence monitoring accuracy reaches 98%.

中文翻译：

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无地面控制点的时间序列无人机摄影测量监测方法，用于测量开采沉陷

地下煤矿开采所引起的地表塌陷及其次级影响不断威胁着矿区居民的安全和财产安全。常用的全站仪和水准测量方法既费时又费力，而且空间范围有限。在这项研究中，FEIMA D2000四旋翼无人机（UAV）用于在工作矿井工作面的表面影响区域（3.75 km2）的四个试验期内进行4-cm /像素摄影测​​量。通过在研究区域的两个子区域中使用五种点云过滤算法进行的性能比较实验表明，ATIN算法为该研究区域提供了最佳的过滤效果。提出了基于时间序列无人机摄影测量技术的山区煤矿塌陷监测。使用这种方法，ATIN算法用于过滤研究区域中的整体点云数据，以获得数字高程模型（DEM）。地表动态沉降盆地由两个阶段的DEM减法确定。结果表明，与同期实测水准数据相比，平均均方根误差为165 mm，最大下沉监测精度达到98％。