Construction Material Classification from Terrestrial Laser Scanning Using a Reflectance-Related Radiometric Descriptor, Multiscale Geometric Roughness Features, and Automated Machine Learning

Construction material identification is important for automated construction monitoring, digital twin generation, and building information modelling. Terrestrial laser scanning (TLS) provides dense geometric information together with LiDAR intensity measurements; however, reliable material discrimination remains challenging because intensity is affected by acquisition geometry and surface texture may vary depending on the spatial scale at which it is characterised. This study proposes a TLS-only machine-learning framework combining a reflectance-related intensity–geometry regression descriptor with multiscale geometric roughness features. Plane-residual roughness and normal-variation roughness were calculated using local cube neighbourhoods with side lengths of 0.10, 0.20, and 0.30 m. To avoid ambiguity associated with surface-normal orientation, the normal-variation descriptor was calculated using orientation-invariant angular differences between neighbouring surface normals. The training dataset was balanced using distance-stratified random undersampling, while the held-out test dataset retained its original class distribution. FLAML was used for automated model selection and hyperparameter optimisation using three-fold cross-validation with macro F1-score as the optimisation metric. The final stacking classifier achieved an overall accuracy of 90.44%, balanced accuracy of 87.42%, and macro F1-score of 87.85% on 1,296,822 held-out test points. The held-out test data were acquired from different scanner positions and spatially distinct material regions from those used for training, with no shared point samples; however, both datasets originated from the same general study area, and broader cross-site generalisation therefore requires further independent validation. Most material classes showed strong discrimination, although Carpet remained challenging because of confusion with Asphalt. The results demonstrate the potential of combining TLS-derived radiometric information with multiscale geometric surface descriptors for construction material classification without relying on RGB colour information.