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Mapping urban villages using fully convolutional neural networks

Mast, Johannes and Wei, Chunzhu and Wurm, Michael (2020) Mapping urban villages using fully convolutional neural networks. Remote Sensing Letters, 11 (7), pp. 630-639. Informa Ltd. DOI: 10.1080/2150704X.2020.1746857 ISSN 2150-704X

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Official URL: https://www.tandfonline.com/doi/full/10.1080/2150704X.2020.1746857

Abstract

Urban villages are a characteristic settlement type characterized by preserving their morphological characteristics embedded in sharp contrast in modern, high-rise developments found especially in fast growing urban agglomerations of China. They serve very important socioeconomic functions in terms of the provision of cheap housing for rural-urban migrants, but they are also considered controversial for local governments. Due to the unprecedented pace of urban growth, especially in the Pearl River Delta region (PRD), up-to-date information on the size and location of urban villages are mostly missing. Large-area but highly detailed data from earth observation platforms can provide crucial information for mapping urban villages based on their characteristic morphologies. This study deploys fully convolutional neural networks for mapping urban villages in the city of Shenzhen. Results of the underlying experiments show that very high mapping accuracies of 84% can be achieved.

Item URL in elib:https://elib.dlr.de/135349/
Document Type:Article
Title:Mapping urban villages using fully convolutional neural networks
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Mast, JohannesJohannes.mast (at) stud-mail.uni-wuerzburg.deUNSPECIFIED
Wei, ChunzhuUNSPECIFIEDUNSPECIFIED
Wurm, Michaelmichael.wurm (at) dlr.dehttps://orcid.org/0000-0001-5967-1894
Date:2020
Journal or Publication Title:Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:11
DOI :10.1080/2150704X.2020.1746857
Page Range:pp. 630-639
Publisher:Informa Ltd
ISSN:2150-704X
Status:Published
Keywords:urban villages, slums, deep learning
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:German Remote Sensing Data Center > Civil Crisis Information and Geo Risks
Deposited By: Wurm, Michael
Deposited On:21 Jul 2020 11:05
Last Modified:06 Oct 2020 08:07

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