Automatic updating of topographic maps remains a significant challenge, as current workflows still rely heavily on manual interpretation of airborne data. This study proposes a method for identifying topographic changes by learning object representations from existing maps and using them as reference data for change detection. Map-derived labels are used to train independent 2D and 3D segmentation networks that generate semantic predictions from orthoimages and point clouds. Unlike conventional change-detection approaches that require temporally aligned datasets of the same modality, the proposed method directly compares newly acquired airborne data with existing map vectors. Semantic predictions from both modalities are vectorized and selectively fused into polygon geometries, which are subsequently compared with reference map vectors to identify object-level “from–to” changes. The workflow highlights potential change regions and their predicted semantic classes, allowing operators to focus inspection on relevant areas rather than the entire dataset. Detected changes include both real-world developments, such as new construction and demolitions, and inconsistencies in the reference map caused by outdated or inaccurate delineations. To assess the effect of multimodal integration, the workflow is compared with a 2D-only baseline. The results indicate that integrating 3D geometric information can reduce noisy detections and improve the spatial consistency of candidate change objects, particularly for water and bridge classes.