An Innovative Approach to the Study of Building Stock Through Benchmark BIG Geodata Mining and Automated LiDAR Roof Reconstruction in Urban Areas: A Case Study from Slovakia

This paper presents BSTwin, a computational workflow and application for the automatic generation of multi-level-of-detail building models and semantically enriched building stock records in urban areas of Slovakia. Its outputs are positioned as the building layer of a city information model (CIM) that can support future urban digital twin applications, not as an operational digital twin. BSTwin couples a lightweight, rule-based roof reconstruction engine for airborne LiDAR point clouds with a benchmark dataset mined from 13 open geodata sources: national registers (INSPIRE HVD Buildings, 2021 CENSUS, INFOREG-EC), European and global datasets (European building stock (EUBUCCO v0.2), Global Human Settlement Layer (GHSL R2023A), Copernicus Urban Atlas, GlobalBuildingAtlas (GBA), OpenBuildingMap (OBM), Microsoft Global Building Footprints) and open-source maps (OpenStreetMap, Overture Maps). Rather than proposing new learned segmentation models, the approach integrates published geometric methods (normal-seeded RANSAC with Hough parameter-space merging, ring-based ground estimation, 3DBAG reference heights, mSTEP azimuth rectification and recursive footprint decomposition) into a deterministic pipeline that runs on an ordinary computer without GIS/BIM software. The term GeoAI is used in its knowledge-driven sense, since no machine-learned model is involved. Each reconstruction and integrated attribute is verified through a quality-control block. On synthetic ground truth, the engine classified all five tested roof archetypes correctly with a point-to-plane RMSE of about 5 cm; on real national LiDAR covering 1468 buildings in the Staré Mesto district of Košice, it reconstructed 99.9% of the buildings, 96.2% of them without a quality flag, in a median of 0.20 s per building. The LiDAR ridge heights agreed with the INSPIRE register heights with a median difference of +0.48 m (65% within 1 m). For the Košice Self-governing Region, the bundled EUBUCCO v0.2 extract covers 488,363 buildings, 81.7% of which have heights of governmental origin. The findings indicate the potential of the workflow for building stock monitoring, renovation planning and as a data foundation for future urban digital twins.