Title
Unsupervised Roofline Extraction from True Orthophotos for LoD2 Building Model Reconstruction
Author
Gao, W. (TU Delft Urban Data Science)
Peters, R.Y. (3DGI) 
Stoter, J.E. (TU Delft Urban Data Science)
Contributor
Kolbe, Thomas H. (editor)
Donaubauer, Andreas (editor)
Beil, Christof (editor)
Date
2024
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.
Subject
Building rooflines extraction
3D building models
True orthophotos
Raytracing
To reference this document use:
http://resolver.tudelft.nl/uuid:93091ce0-2aa5-4b71-854e-43cb9ed37e07
DOI
https://doi.org/10.1007/978-3-031-43699-4_27
Publisher
Springer, Cham
Embargo date
2024-08-21
ISBN
978-3-031-43698-7
Source
Recent Advances in 3D Geoinformation Science: Proceedings of the 18th 3D GeoInfo Conference
Event
18th 3D Geoinfo Conference, 2023-09-12 → 2023-09-14, Technical University of Munich, Munich, Germany
Series
Lecture Notes in Geoinformation and Cartography, 1863-2246
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
conference paper
Rights
© 2024 W. Gao, R.Y. Peters, J.E. Stoter