Augmenting City Models with Handheld LiDAR and 3D Gaussian Splatting for Inclusive Pedestrian Infrastructure Assessment

Deni Suwardhi, Wahyunan Andika, Ratri Widyastuti, Widiaomoko Azis Fadilah, Arnadi Murtiyoso, Pierre Grussenmeyer, Fabio Remondino, Farhan Helmy

Abstract. Urban digital twins increasingly require pedestrian-scale three-dimensional (3D) representations to support accessibility and inclusiveness assessment. However, existing approaches typically emphasize either geometric accuracy or visual realism, while lacking an integrated framework for analysing pedestrian-level conditions. This study proposes a hybrid workflow integrating handheld LiDAR and 3D Gaussian Splatting (3DGS) within a CityGML-based semantic framework for accessibility assessment. Handheld LiDAR provides centimetre-level geometric measurements, enabling the extraction of key indicators such as slope, surface roughness, and obstacle presence. In parallel, 3DGS reconstruction from 360° video imagery enhances visual realism and perceptual understanding. Both datasets are co-registered and structured within the CityGML 3.0 Transportation model to represent pedestrian environments in a unified spatial and semantic framework. Accessibility assessment was conducted using three approaches: LiDAR-based analysis, field survey observations, and immersive evaluation in a Virtual Reality (VR) environment. The LiDAR-based results were used as reference. Comparative analysis shows that field survey assessment achieves an agreement of approximately 85.7%, while VR-based assessment reaches approximately 75.4%. The results indicate that while VR does not replace metric-based analysis, it enables perception-driven and participatory evaluation. In particular, VR-based assessment shows potential to involve users, including people with disabilities, in accessibility evaluation through immersive and remote interaction. The proposed approach contributes to the development of human-scale urban digital twins by integrating metric accuracy, semantic structure, and participatory evaluation for more inclusive accessibility analysis.