Automatic building information model reconstruction in high-density urban areas: Augmenting multi-source data with architectural knowledge

Abstract Many studies have been conducted to create building information models (BIMs) or city information models (CIMs) as the digital infrastructure to support various smart city programs. However, automatic generation of such models for high-density (HD) urban areas remains a challenge owing to (a) complex topographic conditions and noisy data irrelevant to the buildings, and (b) exponentially growing computational complexity when the task is reconstructing hundreds of buildings at an urban scale. This paper develops a method - multi-Source recTification of gEometric Primitives (mSTEP) - for automatic reconstruction of BIMs in HD urban areas. By retrieving building base, height, and footprint geodata from topographic maps, level of detail 1 (LoD1) BIMs representing buildings with flat roof configuration were first constructed. Geometric primitives were then detected from LiDAR point clouds and rectified using architectural knowledge about building geometries (e.g. a rooftop object would normally be in parallel with the outer edge of the roof). Finally, the rectified primitives were used to refine the LoD1 BIMs to LoD2, which show detailed geometric features of roofs and rooftop objects. A total of 1361 buildings located in a four square kilometer area of Hong Kong Island were selected as the subjects for this study. The evaluation results show that mSTEP is an efficient BIM reconstruction method that can significantly improve the level of automation and decrease the computation time. mSTEP is also well applicable to point clouds of various densities. The research is thus of profound significance; other cities and districts around the world can easily adopt mSTEP to reconstruct their own BIMs/CIMs to support their smart city programs.

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