DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Building instance classification using street view images

Kang, Jian and Körner, Marco and Wang, Yuanyuan and Taubenböck, Hannes and Zhu, Xiao Xiang (2018) Building instance classification using street view images. ISPRS Journal of Photogrammetry and Remote Sensing, 145 (A), pp. 44-59. Elsevier. DOI: 10.1016/j.isprsjprs.2018.02.006 ISSN 0924-2716

This is the latest version of this item.

[img] PDF

Official URL: https://www.sciencedirect.com/science/article/pii/S0924271618300352


Land-use classification based on spaceborne or aerial remote sensing images has been extensively studied over the past decades. Such classification is usually a patch-wise or pixel-wise labeling over the whole image. But for many applications, such as urban population density mapping or urban utility planning, a classification map based on individual buildings is much more informative. However, such semantic classification still poses some fundamental challenges, for example, how to retrieve fine boundaries of individual buildings. In this paper, we proposed a general framework for classifying the functionality of individual buildings. The proposed method is based on Convolutional Neural Networks (CNNs) which classify façade structures from street view images, such as Google StreetView, in addition to remote sensing images which usually only show roof structures. Geographic information was utilized to mask out individual buildings, and to associate the corresponding street view images. We created a benchmark dataset which was used for training and evaluating CNNs. In addition, the method was applied to generate building classification maps on both region and city scales of several cities in Canada and the US.

Item URL in elib:https://elib.dlr.de/124194/
Document Type:Article
Additional Information:so2sat; relevancy 4;
Title:Building instance classification using street view images
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Körner, Marcomarco.koerner (at) tum.deUNSPECIFIED
Wang, Yuanyuantumhttps://orcid.org/0000-0002-0586-9413
Taubenböck, Hanneshannes.taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126
Zhu, Xiao Xiangdlr-imf/tum-lmfUNSPECIFIED
Date:2 March 2018
Journal or Publication Title:ISPRS Journal of Photogrammetry and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1016/j.isprsjprs.2018.02.006
Page Range:pp. 44-59
Keywords:CNN, individual building classification, building instance, street view image, deep learning, remote sensing, data fusion
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
German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Wang, Yuanyuan
Deposited On:04 Dec 2018 13:08
Last Modified:06 Sep 2019 15:28

Available Versions of this Item

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.