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Building Footprint Generation using Improved Generative Adversarial Networks

Shi, Yilei and Li, Qingyu and Zhu, Xiao Xiang (2019) Building Footprint Generation using Improved Generative Adversarial Networks. IEEE Geoscience and Remote Sensing Letters, 16 (4), pp. 603-607. IEEE - Institute of Electrical and Electronics Engineers. ISSN 1545-598X

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Official URL: https://ieeexplore.ieee.org/document/8581486

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 letter, we have proposed improved generative adversarial networks (GANs) for the automatic generation of building footprints from satellite images. We used a conditional GAN (CGAN) 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 CGANs, the U-Net, and other networks. In addition, our method nearly removes all hyperparameters tuning.

Item URL in elib:https://elib.dlr.de/122453/
Document Type:Article
Title:Building Footprint Generation using Improved Generative Adversarial Networks
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Shi, YileiTU-MünchenUNSPECIFIED
Li, QingyuTU-MünchenUNSPECIFIED
Zhu, Xiao XiangDLR-IMF/TUM-LMFUNSPECIFIED
Date:2019
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:16
Page Range:pp. 603-607
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:Published
Keywords:Building footprint, conditional generative adversarial networks (CGANs), generative adversarial networks (GANs), segmentation, Wasserstein GANs (WGANs)
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 - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Hoffmann, Eike Jens
Deposited On:23 Oct 2018 14:50
Last Modified:11 Feb 2020 08:48

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