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Building Footprint Generation Through Convolutional Neural Networks With Attraction Field Representation

Li, Qingyu and Mou, LiChao and Hua, Yuansheng and Shi, Yilei and Zhu, Xiao Xiang (2022) Building Footprint Generation Through Convolutional Neural Networks With Attraction Field Representation. IEEE Transactions on Geoscience and Remote Sensing. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2021.3109844. ISSN 0196-2892. (In Press)

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Official URL: https://ieeexplore.ieee.org/document/9538384


Building footprint generation is a vital task in a wide range of applications, including, to name a few, land use management, urban planning and monitoring, and geographical database updating. Most existing approaches addressing this problem fall back on convolutional neural networks (CNNs) to learn semantic masks of buildings. However, one limitation of their results is blurred building boundaries. To address this, we propose to learn attraction field representation for building boundaries, which is capable of providing an enhanced representation power. Our method comprises two elemental modules: an Img2AFM module and an AFM2Mask module. More specifically, the former aims at learning an attraction field representation conditioned on an input image, which is capable of enhancing building boundaries and suppressing the background. The latter module predicts segmentation masks of buildings using the learned attraction field map. The proposed method is evaluated on three datasets with different spatial resolutions: the ISPRS dataset, the INRIA dataset, and the Planet dataset. From experimental results, we find that the proposed framework can well preserve geometric shapes and sharp boundaries of buildings, which brings significant improvements over other competitors. The trained model and code are available at https://github.com/lqycrystal/AFM_building

Item URL in elib:https://elib.dlr.de/145755/
Document Type:Article
Title:Building Footprint Generation Through Convolutional Neural Networks With Attraction Field Representation
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Li, QingyuQingyu.Li (at) dlr.deUNSPECIFIED
Mou, LiChaoLiChao.Mou (at) dlr.dehttps://orcid.org/0000-0001-8407-6413
Hua, YuanshengYuansheng.Hua (at) dlr.deUNSPECIFIED
Shi, Yileiyilei.shi (at) tum.deUNSPECIFIED
Zhu, Xiao Xiangxiaoxiang.zhu (at) dlr.dehttps://orcid.org/0000-0001-5530-3613
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/TGRS.2021.3109844
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Status:In Press
Keywords:Attraction field map (AFM) building footprint , convolutional neural network (CNN) , semantic segmentation.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Rösel, Anja
Deposited On:18 Nov 2021 13:38
Last Modified:14 Jan 2022 14:55

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