M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation [Data and Source Code]

The analysis of topographic time series is often based on bitemporal change detection and quantification. For 3D point clouds, acquired using laser scanning or photogrammetry, random and systematic noise has to be separated from the signal of surface change by determining the minimum detectable change. To analyse geomorphic change in point cloud data, the multiscale model-to-model cloud comparison (M3C2) approach is commonly applied, which provides a statistical significance test. This test assumes planar surfaces and a uniform registration error. For natural surfaces, the planarity assumption does not necessarily apply, in which cases the value of minimal detectable change (Level of Detection) is overestimated. To overcome these limitations, we quantify an uncertainty information for each 3D point by propagating the uncertainty of the measurements themselves and of the alignment uncertainty to the 3D points. This allows the calculation of 3D covariance information for the point cloud, which we use in an extended statistical test for equality of multivariate means. Our method, called M3C2-EP, gives a less biased estimate of the Level of Detection, allowing a more appropriate significance threshold in typical cases. We verify our method in two simulated scenarios, and apply it to a time series of terrestrial laser scans of a rock glacier at two different timespans of three weeks and one year. Over the three-week period, we detect significant change at 12.5% fewer 3D locations, while quantifying additional 25.2% of change volume, when compared to the reference method of M3C2. Compared with manual assessment, M3C2-EP achieves a specificity of 0.97, where M3C2 reaches 0.86 for the one year timespan, while sensitivity drops from 0.72 for M3C2 to 0.60 for M3C2-EP. Lower Levels of Detection enable the analysis of high-frequency monitoring data, where usually less change has occurred between successive scans, and where change is small compared to local roughness. Our method further allows the combination of data from multiple scan positions or data sources with different levels of uncertainty. The combination using error propagation ensures that every dataset is used to its full potential. --- This dataset includes three point clouds acquired by terrestrial laser scanning in 2017 and 2018, as well as alignment information (ICPout) and the code used for processing the datasets. Unzipping all the folders to the same directory should allow you to run the python script as-is. Point clouds have been pre-processed using the following workflow: 1) MSA coregistration within every epoch using RiScan Pro v2.7 2) MSA coregistration within across epochs on stable areas using RiScan Pro v2.7 3) ICP coregistration in opalsICP v2.3.1, resulting in the data in ICPout 4) Point cloud filterting using PDAL v2.2.0 ("filters.pmf") 5) Tiling into 100m tiles (5m overlap) using lastile地形时间序列分析通常基于双时相变化检测与量化。针对通过激光扫描或摄影测量获取的三维点云，需通过确定最小可检测变化量，将随机与系统噪声从地表变化信号中分离出来。 为分析点云数据中的地貌变化，多尺度模型间点云比较（multiscale model-to-model cloud comparison, M3C2）方法被广泛应用，该方法可提供统计显著性检验。此检验假设地表为平面且配准误差均匀分布。但对于自然地表，平面假设并不一定成立，此时最小可检测变化（检测水平，Level of Detection）的估值会被高估。 为克服这些局限，我们通过将测量自身的不确定性与配准不确定性传播至三维点，为每个三维点量化不确定性信息。这可实现点云的三维协方差信息计算，我们将其用于扩展的多元均值相等性统计检验中。我们的方法名为M3C2-EP，可生成偏倚性更低的检测水平估计值，在典型场景下可设置更合理的显著性阈值。 我们在两种模拟场景中验证了该方法，并将其应用于两个时间跨度（三周与一年）的石冰川地面激光扫描时间序列数据。相较于参考方法M3C2，在三周时间跨度内，我们检测到的显著变化三维点位减少了12.5%，但量化得到的变化体积增加了25.2%。与人工评估结果相比，在一年时间跨度的场景中，M3C2-EP的特异性可达0.97，而M3C2仅为0.86；其灵敏度则从M3C2的0.72降至0.60。更低的检测水平可支撑高频监测数据的分析——这类场景中连续扫描间的变化通常较小，且相较于局部地表粗糙度，变化幅度微弱。我们的方法还支持整合来自多个扫描站位或不同不确定性水平数据源的数据，通过误差传播进行整合可确保每份数据集都得到充分利用。 本数据集包含2017年与2018年通过地面激光扫描获取的三维点云，以及配准信息（ICPout）与数据集处理所用代码。将所有文件夹解压至同一目录后，即可直接运行Python脚本。点云已通过以下流程完成预处理： 1. 使用RiScan Pro v2.7对每个观测时段内的点云进行多站平差（Multi-Station Adjustment, MSA）配准 2. 使用RiScan Pro v2.7对稳定区域的跨观测时段点云进行多站平差配准 3. 使用opalsICP v2.3.1进行迭代最近点（Iterative Closest Point, ICP）配准，得到ICPout文件夹中的数据 4. 使用PDAL（Point Data Abstraction Library）v2.2.0的"filters.pmf"滤波器进行点云滤波 5. 使用lastile工具将点云切分为100m×100m的瓦片，重叠度为5m