Machine-learned 3D Building Vectorization from Satellite Imagery

• URL: http://arxiv.org/abs/2104.06485v1
• Date: Tue, 13 Apr 2021 19:57:30 GMT
• Title: Machine-learned 3D Building Vectorization from Satellite Imagery
• Authors: Yi Wang, Stefano Zorzi, Ksenia Bittner
• Abstract summary: We propose a machine learning based approach for automatic 3D building reconstruction and vectorization. Taking a single-channel photogrammetric digital surface model (DSM) and panchromatic (PAN) image as input, we first filter out non-building objects and refine the building of shapes. The refined DSM and the input PAN image are then used through a semantic segmentation network to detect edges and corners of building roofs.
• Score: 7.887221474814986
• License: http://creativecommons.org/licenses/by/4.0/
• 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 panchromatic (PAN) image as input, we first filter out non-building objects and refine the building shapes of 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 the refined DSM is 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.

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