ORIGINAL RESEARCH article

Front. Built Environ.

Sec. Transportation and Transit Systems

Volume 11 - 2025 | doi: 10.3389/fbuil.2025.1607375

This article is part of the Research TopicDigital Transformation in Construction: Integrating Metaverse, Digital Twin, and BIMView all 7 articles

Data Enrichment for Semantic Segmentation of Point Clouds for the Generation of Geometric-Semantic Road Models

Provisionally accepted
David  CrampenDavid Crampen1,2*Joerg  Manfred BlankenbachJoerg Manfred Blankenbach1,2
  • 1RWTH Aachen University, Aachen, Germany
  • 2Geodetic Institute and Chair for Computing in Civil Engineering & GIS, Civil Engineering Department, RWTH Aachen University, Aachen, Germany

The final, formatted version of the article will be published soon.

Digitalizing highway infrastructure is gaining interest in Germany and other countries due to the need for greater efficiency and sustainability. The maintenance of the built infrastructure accounts for nearly 30% of greenhouse gas emissions in Germany. To address this, Digital Twins are emerging as tools to optimize road systems. A Digital Twin of a built asset relies on a geometric-semantic as-is model of the area of interest, where an essential step for automated model generation is the semantic segmentation of reality capture data. While most approaches handle data without considering real-world context, our approach leverages existing geospatial data to enrich the data foundation through an adaptive feature extraction workflow. This workflow is adaptable to various model architectures, from deep learning methods like PointNet++ and PointNeXt to traditional machine learning models such as Random Forest. Our four-step workflow significantly boosts performance, improving overall accuracy by 20% and unweighted mean Intersection over Union (mIoU) by up to 43.47%. The target application is the semantic segmentation of point clouds in road environments. Additionally, the proposed modular workflow can be easily customized to fit diverse data sources and enhance semantic segmentation performance in a model-agnostic way.

Keywords: Point Clouds, Semantic segmentation, deep learning, machine learning, Geometric-Semantic Road Models, Automation, Digital Twin, Data enrichment

Received: 07 Apr 2025; Accepted: 22 May 2025.

Copyright: © 2025 Crampen and Blankenbach. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: David Crampen, RWTH Aachen University, Aachen, Germany

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