In this paper, we present a novel approach for semantic segmentation of airborne LiDAR point clouds that integrates a hierarchical Gaussian Mixture Model (hGMM) within the Superpoint Transformer (SPT) framework. The hGMM constructs a coarse-to-fine representation of the scene by recursively fitting Gaussian components to spatially coherent subsets of the point cloud, resulting in a hierarchical and structured decomposition that serves as a structured token set for the segmentation objective. While Gaussian Mixture Models (GMMs) can virtually fit any distribution, we constrain their use to structured suburban scenes, where their parametric form is naturally suited to represent planar and ellipsoidal geometries, hence allowing parsimonious mixtures. Experimental results on the DALES benchmark demonstrate that our method achieves competitive performance with respect to state-of-the-art approaches, with notable improvements on classes such as ground and buildings. Results on indoor S3DIS confirm the method’s intended specificity to outdoor environments. These findings validate hGMM as a principled and effective alternative to heuristic partitioning techniques, integrating stochastic modelling with transformer-based semantic reasoning in large-scale 3D environments.