@article{Ding2026, 
author = {Yuexiong Ding and Mengtian Yin and Ran Wei and Ioannis Brilakis and Muyang Liu and Xiaowei Luo},
title = {Automatic road digital twinning from semantically labeled point cloud data},
year = {2026},
journal = {Journal of Intelligent Construction},
volume = {4},
number = {1},
pages = {9180112},
keywords = {geometric digital twin, point cloud data, as-built road, scan-to-BIM, sectional polygon contour, geometric information extraction},
url = {https://www.sciopen.com/article/10.26599/JIC.2026.9180112},
doi = {10.26599/JIC.2026.9180112},
abstract = {Creating geometric digital twins (gDTs) for as-built roads still has many limitations, such as low automation level and accuracy, limited asset types and shapes, and reliance on engineering experience. A novel scan-to-building information modeling (scan-to-BIM) framework is proposed for automatic road gDT creation based on semantically labeled point cloud data (PCD), which considers six asset types: road surface, road side (slope), road lane (marking), road/traffic sign, road/street light, and guardrail. The framework first segments the semantic PCD into spatially independent instances or parts, and then extracts the sectional polygon contours as their representative geometric information, stored in JavaScript Object Notation (JSON) files using a new data structure. Primitive gDTs are finally created from the JSON files using the corresponding conversion algorithms. The proposed method achieves an average distance error of 1.46 cm and a processing speed of 6.29 m/s on six real-world road segments with a total length of 1200 m.}
}