Machine-Learned 3D Building Vectorization from Satellite Imagery
Abstract
We propose a machine learning based approach for automatic 3D building reconstruction and vectorization. Taking a single-channel photogrammetric digital surface model (DSM) and a panchromatic (PAN) image as input, we first filter out non-building objects and refine the building shapes of the input DSM with a conditional generative adversarial network (cGAN). The refined DSM and the input PAN image are then used through a semantic segmentation network to detect edges and corners of building roofs. Later, a set of vectorization algorithms are proposed to build roof polygons. Finally, the height information from refined DSM is processed and added to the polygons to obtain a fully vectorized level of detail (LoD)-2 building model. We verify the effectiveness of our method on large-scale satellite images, where we obtain state-of-the-art performance.
Cite
Text
Wang et al. "Machine-Learned 3D Building Vectorization from Satellite Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021. doi:10.1109/CVPRW53098.2021.00118Markdown
[Wang et al. "Machine-Learned 3D Building Vectorization from Satellite Imagery." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2021.](https://mlanthology.org/cvprw/2021/wang2021cvprw-machinelearned/) doi:10.1109/CVPRW53098.2021.00118BibTeX
@inproceedings{wang2021cvprw-machinelearned,
title = {{Machine-Learned 3D Building Vectorization from Satellite Imagery}},
author = {Wang, Yi and Zorzi, Stefano and Bittner, Ksenia},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2021},
pages = {1072-1081},
doi = {10.1109/CVPRW53098.2021.00118},
url = {https://mlanthology.org/cvprw/2021/wang2021cvprw-machinelearned/}
}