RoadGen4Twins: A Modular Approach for Generating Multi-Purpose Geometric-Semantic Models for Digital Twins of Roads

ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, vol. X-4/W5-2024, pp. 103–110

Abstract

Abstract. The development of novel and robust digital methods to support the maintenance of existing road infrastructure requires a large amount of harmonized data. Especially in the context of automated modelling having a large amount of matching data from different perspectives enables disruptive, new use cases that might largely impact the efficiency in maintenance of the built environment. Unfortunately, such data compositions are tedious to collect in real world applications, due to many influential factors, leading to deviations between multiple data sources and the sheer complexity in the process of creating a digital model. However, for deep learning applications, a large amount of carefully annotated data is necessary for robust estimations. In this contribution, we tackle this problem by presenting a novel procedural modelling and model configuration approach for generating homogeneous data combinations to step towards direct parameter estimation for machine learning approaches utilizing point clouds of roads and end-to-end model generation of digital road models.

Authors 3

1. David Crampen Aachen Geodetic Institute

   RWTH Aachen University

   Affiliation as printed

   Geodetic Institute, RWTH Aachen University, Germany

2. Marcel Hein Aachen Geodetic Institute

   RWTH Aachen University

   Affiliation as printed

   Geodetic Institute, RWTH Aachen University, Germany

3. Jörg Blankenbach Aachen Geodetic Institute

   RWTH Aachen University

   Affiliation as printed

   Geodetic Institute, RWTH Aachen University, Germany