Unsupervised Roofline Extraction from True Orthophotos for LoD2 Building Model Reconstruction

TL;DR

This paper introduces a novel method for extracting rooflines from true orthophotos to improve LoD2 building model reconstruction, offering higher accuracy and completeness without needing pre-labeled data or deep learning models.

Contribution

The proposed line detection approach effectively extracts rooflines from orthophotos, surpassing traditional plane detection and deep learning methods in accuracy and completeness.

Findings

Outperforms existing methods in roofline extraction accuracy.

Produces more complete building models at LoD2 level.

Does not require pre-labeled training data or pre-trained models.

Abstract

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.