Integrating geospatial data and street‐view imagery to reconstruct large‐scale 3D urban building models

Abstract

3D urban building modeling is a vital foundational step for building Digital Twins and Smart Cities. In response to existing challenges, such as high time costs, complex production processes, and low consistency with real‐world textures in large‐scale 3D urban building modeling methods, this research proposes a reconstructing 3D urban building models (3DUBM) approach that integrates geospatial data and street view. The approach achieves an enhanced generation of large‐scale 3DUBMs. Based on open geospatial data and street‐view imagery (SVI), the approach was tested in modeling experiments conducted in Shanghai, Hongkong, and Nanjing. Furthermore, a dataset covering unique blocks of 30 cities in China was constructed to demonstrate the approach's characteristics of large coverage, high time efficiency, high model quality and low economic cost. The accuracy of texture mapping from SVI to 3DUBM reached 85%. This achievement has significant economic value in bridging the gap in the production of large‐scale and low‐cost 3DUBM data, promoting the construction of Digital Twins, Smart Cities, and Real‐world 3D modeling.