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

Three-dimensional (3D) city models offer visual representation, interaction, analysis, and exploration of urban landscapes. However, most cities do not have open 3D city model datasets. This study used large-scale high-density airborne LiDAR point clouds to produce 3D building and tree datasets 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 city model datasets include 3D building and tree data. 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 city model data can be used in multiple environmental applications in Glasgow, and the open-source data generation workflow can be extended to other major cities for similar applications.