End-to-end roofline extraction from very-high-resolution remote sensing images

Wufan Zhao, C. Persello, A. Stein

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

2 Citations (Scopus)
174 Downloads (Pure)

Abstract

Roof shape information is essential for creating 3D building models. However, the automated extracting of roof structures from Earth observation data is a difficult task involving significant uncertainties caused by scene complexity and limited multi-source data coverage. This paper introduces the integrally-attracted wireframe parsing (IAWP) framework to reconstruct building rooflines as a planar graph from remotely sensed images with a single forward pass. We add global geometric line priors through the Hough transform into deep networks to better extract the linear geometric features. We perform experiments on the vectorizing world building (VWB) dataset. The investigated method improves the F-score metrics of corner points/edges by 0.1%/7.7% and 0.6%/1.1%, respectively. Visual comparison results also indicate that the HT-IHT block gives consistent improvements in terms of geometric regularity.

Original languageEnglish
Title of host publication2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages2783-2786
Number of pages4
ISBN (Electronic)978-1-6654-0369-6
ISBN (Print)978-1-6654-4762-1, 978-1-6654-0368-9 (USB)
DOIs
Publication statusPublished - 12 Oct 2021
Event2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Brussels, Belgium
Duration: 12 Jul 202116 Jul 2021
https://igarss2021.com

Conference

Conference2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Abbreviated titleIGARSS 2021
Country/TerritoryBelgium
CityBrussels
Period12/07/2116/07/21
Internet address

Keywords

  • Building roofline extraction
  • End-to-end learning
  • Hough-Transformation
  • integrated attraction field
  • 2024 OA procedure

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