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 language | English |
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Title of host publication | 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS |
Place of Publication | Piscataway, NJ |
Publisher | IEEE |
Pages | 2783-2786 |
Number of pages | 4 |
ISBN (Electronic) | 978-1-6654-0369-6 |
ISBN (Print) | 978-1-6654-4762-1, 978-1-6654-0368-9 (USB) |
DOIs | |
Publication status | Published - 12 Oct 2021 |
Event | 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021 https://igarss2021.com |
Conference
Conference | 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 |
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Abbreviated title | IGARSS 2021 |
Country/Territory | Belgium |
City | Brussels |
Period | 12/07/21 → 16/07/21 |
Internet address |
Keywords
- Building roofline extraction
- End-to-end learning
- Hough-Transformation
- integrated attraction field
- 2024 OA procedure