Large-scale 3D building model datasets constructed from airborne LiDAR point clouds in Glasgow, UK

Source abstract

Three-dimensional (3D) building models offer visual representation, interaction, analysis, and exploration of urban environment analysis. However, most cities in the United Kingdom (UK) do not have open building 3D model datasets. This study used large-scale high-density airborne LiDAR point clouds to produce 3D building models for Glasgow City. We proposed an open-source and efficient data analysis workflow that integrated a weakly supervised deep learning point cloud classification algorithm and a data-driven 3D model reconstruction method. The Glasgow 3D building model datasets include building footprints with height attributes and 3D models in the level of detail 1 (LoD1) and LoD2. The cross-reference results show that our building footprint aligned well with UK Ordnance Survey data (intersection over union of 82.67% for overlay, R = 0.93 and RMSE = 1.84 m for building height). Building models well represent outer shell features with an average RMSE = 0.54 m for the distance between point clouds and reconstructed models. This accurate 3D building model data can be used in multiple environmental applications for Glasgow, and the open-source data generation workflow can be extended to other major cities for similar applications.