Model-Driven Reconstruction of 3-D Buildings Using LiDAR Data

Data-driven and model-driven strategies are two basic approaches for building reconstruction based on LiDAR data. Due to the data noise and limitations in existing algorithms, the data-driven approach can neither construct a complete roof plane nor construct irregular planes. This letter proposes a model-driven approach to reconstruct 3-D building structures by developing prototypical roofs for commercial and residential buildings. The experiment was conducted in the City of Indianapolis, IN, USA, using the LiDAR data and building footprints provided by the city government. Irregular building footprints were first decomposed into nonintersecting and mostly quadrangular blocks for the identification of the most probable prototypical roofs. A decision tree classifier was applied to classify all building footprints into seven subtypes based on the physical and morphological parameters of buildings. The modeling of prototypical roofs was finished with the parameters including the length, the width, and the orientation of the principal axis of each building block that were computed from the LiDAR data using the static moment equations. Additional parameters, including the mean height, the top height, and the gutter height, were also considered as needed for some subtypes. Complex roofs were reconstructed by assembling adjacent prototypical roofs. The decision tree classification method was finally applied to 268 building blocks and achieved an overall accuracy of 82.1% for seven classes. A 3-D geographic information system building database that includes commercial and residential buildings in two chosen city blocks was created for further applications. This letter created a more completed building roof structure than the existing data-driven approach and demonstrated the reliability of a decision tree classifier in categorizing building roofs.