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BFGAN - Building Footprint Extraction from Satellite Images

Shi, Yilei and Li, Qingyu and Zhu, Xiao Xiang (2019) BFGAN - Building Footprint Extraction from Satellite Images. In: 2019 Joint Urban Remote Sensing Event, JURSE 2019, pp. 1-4. IEEE. JURSE 2019, 22.-24. Mai 2019, Vannes, FR. DOI: 10.1109/JURSE.2019.8809048

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Official URL: https://ieeexplore.ieee.org/document/8809048

Abstract

Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. In this work, we have proposed improved generative adversarial networks (GANs) for the automatic generation of building footprints from satellite images. We used a conditional GAN with a cost function derived from the Wasserstein distance and added a gradient penalty term. The achieved results indicated that the proposed method can significantly improve the quality of building footprint generation compared to conditional generative adversarial networks, the U-Net, and other networks. In addition, our method nearly removes all hyperparameter tuning.

Item URL in elib:https://elib.dlr.de/134416/
Document Type:Conference or Workshop Item (Speech, Poster)
Title:BFGAN - Building Footprint Extraction from Satellite Images
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Shi, Yileiyilei.shi (at) tum.deUNSPECIFIED
Li, Qingyuqingyu.li (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:May 2019
Journal or Publication Title:2019 Joint Urban Remote Sensing Event, JURSE 2019
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI :10.1109/JURSE.2019.8809048
Page Range:pp. 1-4
Publisher:IEEE
Status:Published
Keywords:building footprint, generative adversarial networks (GANs), conditional generative adversarial networks (CGANs), Wasserstein generative adversarial networks (WGANs)
Event Title:JURSE 2019
Event Location:Vannes, FR
Event Type:international Conference
Event Dates:22.-24. Mai 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Haschberger, Dr.-Ing. Peter
Deposited On:12 Mar 2020 11:44
Last Modified:12 Mar 2020 11:44

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