Machine-learned 3D building vectorization from satellite imagery

Yi Wang, Stefano Zorzi, Ksenia Bittner

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
PublisherIEEE Computer Society
Pages1072-1081
Number of pages10
ISBN (Electronic)9781665448994
DOIs
Publication statusPublished - Jun 2021
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops: CVPRW 2021 - Virtuell, United States
Duration: 19 Jun 202125 Jun 2021

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
Abbreviated titleCVPRW 2021
Country/TerritoryUnited States
CityVirtuell
Period19/06/2125/06/21

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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