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.