A TLS-Based Framework for the Realization of Digital Twin Basemaps Applied to an Adaptively Reused Heritage Building

Abstract

The transition toward urban-scale digital twin and smart city management requires survey-grade 3D basemaps, yet conventional documentation remains time-consuming and prone to inaccuracies. While Terrestrial Laser Scanning (TLS) offers rapid 3D acquisition, capturing complex, GNSS-denied multi-story interiors frequently causes cumulative registration errors and isolated indoor–outdoor data silos. To address this, this study proposes a comprehensive typology-agnostic framework for developing high-fidelity digital twin basemaps. Treating the building as a unified spatial network, the methodology systematically mitigates error propagation through strategic linkage planning, rigid shell-first registration, continuous vertical core anchoring (via stairwells), and adaptive multi-source data fusion. Implemented on an adaptively reused heritage building, the developed basemap achieved an absolute georeferencing accuracy of 30.0 mm (RMSE) against an independent total station control network, alongside a mean relative error of 3.11 mm. Comparative analysis against legacy 2D CAD floor plans revealed simplified geometric representations and categorical dimensional deviations of up to 29.8 cm. Demonstrating its practical utility, the point cloud-centric geometric hub avoids forced geometric idealization, successfully supporting direct immersive visualization, architectural slicing (floor plans, sections, elevations), and multi-LOD algorithmic planar segmentation. This spatially constrained acquisition strategy bypasses legacy limitations, delivering a mathematically verified 3D reality capture essential for smart facility management, heritage conservation, and downstream semantic intelligence.