Machine-learned 3D Building Vectorization from Satellite Imagery
Yi Wang, Stefano Zorzi, Ksenia Bittner
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.