Unsupervised Roofline Extraction from True Orthophotos for LoD2 Building
  Model Reconstruction
Weixiao Gao Ravi Peters Jantien Stoter
This paper discusses the reconstruction of LoD2 building models from 2D and
3D data for large-scale urban environments. Traditional methods involve the use
of LiDAR point clouds, but due to high costs and long intervals associated with
acquiring such data for rapidly developing areas, researchers have started
exploring the use of point clouds generated from (oblique) aerial images.
However, using such point clouds for traditional plane detection-based methods
can result in significant errors and introduce noise into the reconstructed
building models. To address this, this paper presents a method for extracting
rooflines from true orthophotos using line detection for the reconstruction of
building models at the LoD2 level. The approach is able to extract relatively
complete rooflines without the need for pre-labeled training data or
pre-trained models. These lines can directly be used in the LoD2 building model
reconstruction process. The method is superior to existing plane
detection-based methods and state-of-the-art deep learning methods in terms of
the accuracy and completeness of the reconstructed building. Our source code is
available at https://github.com/tudelft3d/Roofline-extraction-from-orthophotos.