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Songnian Li

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Review Open access Aug 2026

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

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.

Mohamed H. Salaheldin, A. Shaker, Songnian Li · 0 citations
Open access Aug 2026

Multi-Sensor Spatial Data Fusion for Road Condition Monitoring Digital Twins

Abstract. Pavement Management Systems (PMS) are essential for evaluating and maintaining transportation infrastructure; however, conventional monitoring methods are often labour-intensive, costly, and inaccurate. The growing need for reliable. timely pavement condition data has driven the development of automated, data-driven approaches. This study presents a low-cost and scalable framework for pavement condition monitoring that integrates multimodal sensing with a digital twin (DT) environment. Smartphones equipped with inertial measurement unit (IMU) sensors, GPS, and cameras are used to collect synchronized vibration and visual data during normal driving conditions. Vibration signals are analysed to detect anomalies associated with pavement surface irregularities, while video data are processed using a deep learning-based object detection model to identify surface distress. A late fusion approach combines the outputs from both modalities to improve detection reliability and provide comprehensive condition assessment. The system enables spatial mapping of detected distresses and supports real-time visualization through a web-based DT dashboard. Results demonstrate that multimodal sensing compensates for the limitations of individual sensors, enhancing both detection accuracy and robustness. The proposed framework offers a practical solution for efficient pavement monitoring. It supports data-driven decision-making for proactive infrastructure management, with potential for future expansion through crowdsourced data and additional sensing technologies.

Deepak Satheesan, Songnian Li, Michael A. Chapman · 0 citations