High-definition 3D city maps enable city planning and change detection, which is essential for municipal compliance, map maintenance, and asset monitoring, including both built structures and urban greenery. Conventional Digital Surface Model (DSM) and image differencing are sensitive to vertical bias and viewpoint mismatch, while original point cloud or voxel models require large memory, assume perfect alignment, and degrade thin structures. We propose an uncertainty-aware, object-centric method for city-scale LiDAR-based change detection. Our method aligns data from different time periods using multi-resolution Normal Distributions Transform (NDT) and a point-to-plane Iterative Closest Point (ICP) method, normalizes elevation, and computes a per-point level of detection from registration covariance and surface roughness to calibrate change decisions. Geometry-based associations are refined by semantic and instance segmentation and optimized using class-constrained bipartite assignment with augmented dummies to handle split-merge cases. Tiled processing bounds memory and preserves narrow ground changes, while instance-level decisions integrate overlap, displacement, and volumetric differences under local detection gating. We perform experiments on a Subiaco (Western Australia) dataset captured in 2023 and again in 2025. Our method achieves 95.3% accuracy, 90.8% mF1, and 82.9% mIoU, improving over the strongest baseline, Triplet KPConv, by 0.3, 0.6, and 1.1 points, respectively. The datasets are available on IEEE DataPort (2023: https://ieee-dataport.org/documents/2023-subiaco-wa-3d-hd-lidar-point-cloud-maps-dataset and 2025: https://ieee-dataport.org/documents/2025-subiaco-wa-3d-hd-lidar-gnss-point-cloud-maps-dataset). The source code is available at https://github.com/HaitianWang/IEEE-Sensor-Journal-Changing-Detection.

翻译：高清晰度三维城市地图支持城市规划和变化检测，这对于市政合规、地图维护以及建筑结构与城市绿化等资产监测至关重要。传统的数字表面模型（DSM）和影像差分方法对垂直偏差和视点失配敏感，而原始点云或体素模型则需要大量内存、假设完美配准，且会弱化薄结构。我们提出一种基于不确定性感知、以对象为中心的城市尺度LiDAR变化检测方法。该方法使用多分辨率正态分布变换（NDT）和点对平面迭代最近点（ICP）方法对不同时期的数据进行配准，归一化高程，并根据配准协方差与表面粗糙度计算逐点检测水平以校准变化决策。基于几何的关联通过语义与实例分割进行细化，并利用带增强虚拟节点的类约束二分图分配进行优化，以处理分裂-合并情况。分块处理限制了内存使用并保留了狭窄的地面变化，而实例级决策在局部检测门控下整合了重叠度、位移和体积差异。我们在2023年与2025年分别采集的Subiaco（西澳大利亚）数据集上进行了实验。我们的方法实现了95.3%的准确率、90.8%的宏观F1分数和82.9%的宏观交并比，分别比最强基线Triplet KPConv提高了0.3、0.6和1.1个百分点。数据集可在IEEE DataPort获取（2023年：https://ieee-dataport.org/documents/2023-subiaco-wa-3d-hd-lidar-point-cloud-maps-dataset，2025年：https://ieee-dataport.org/documents/2025-subiaco-wa-3d-hd-lidar-gnss-point-cloud-maps-dataset）。源代码发布于https://github.com/HaitianWang/IEEE-Sensor-Journal-Changing-Detection。