Automatic alignment of semantic building models with digital city models through multi-level point cloud semantic segmentation and registration using PSO optimization: A case study of the TUM main campus

Multi-scale Digital Twins (DTs) of the built environment provide valuable spatial and semantic insights into city assets, supporting urban management, sustainability, and mobility. However, aligning semantic building models with large-scale digital city models remains a significant challenge due to discrepancies in geospatial alignment, spatial resolution, and heterogeneity of data sources. This paper proposes a novel pipeline for the automatic alignment of semantic indoor building models with digital city models. The pipeline comprises two main steps: first, multi-level semantic segmentation of using Artificial Intelligence (AI) models, and second, point cloud registration using the Particle Swarm Optimization (PSO) algorithm, which estimates the transformation parameters required to align the corresponding models. The results of testing the proposed pipeline on real-world data from the Technical University of Munich (TUM) demonstrate the effectiveness of the proposed method in aligning semantic digital models across multiple scales. • Automated pipeline for alignment of indoor building models with digital city models. • AI-based semantic segmentation and PSO-based optimization for point cloud registration. • Extraction of semantic information from point clouds at indoor, façade, and city scales. • Multi-resolution point cloud registration across various spatial scales. • Enhance geometric accuracy in indoor building models through façade wall width estimation.