Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China

Scientific functional zone planning is the key to achieving long-term development goals for cities. The rapid development of remote sensing technology allows for the identification of urban functional zones, which is important since they serve as basic spatial units for urban planning and functionin...

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Autores principales:	Yuewen Yang, Dongyan Wang, Zhuoran Yan, Shuwen Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	urban functional zone U-Net spatial distribution Kuancheng District Agriculture S
Acceso en línea:	https://doaj.org/article/913d461866ad4b7384ae17a095756c78
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spelling	oai:doaj.org-article:913d461866ad4b7384ae17a095756c782021-11-25T18:10:15ZDelineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China10.3390/land101112662073-445Xhttps://doaj.org/article/913d461866ad4b7384ae17a095756c782021-11-01T00:00:00Zhttps://www.mdpi.com/2073-445X/10/11/1266https://doaj.org/toc/2073-445XScientific functional zone planning is the key to achieving long-term development goals for cities. The rapid development of remote sensing technology allows for the identification of urban functional zones, which is important since they serve as basic spatial units for urban planning and functioning. The accuracy of three methods—kernel density estimation, term frequency-inverse document frequency, and deep learning—for detecting urban functional zones was investigated using the Gaode points of interest, high-resolution satellite images, and OpenStreetMap. Kuancheng District was divided into twenty-one functional types (five single functional types and twenty mixed ones). The results showed that an approach using deep learning had a higher accuracy than the other two methods for delineating four out of five functions (excluding the commercial function) when compared with a field survey. The field survey showed that Kuancheng District was progressing towards completing the goals of the Land-Use Plan of the Central City of Changchun (2011–2020). Based on these findings, we illustrate the feasibility of identifying urban functional areas and lay out a framework for transforming them. Our results can guide the adjustment of the urban spatial structure and provide a reference basis for the scientific and reasonable development of urban land-use planning.Yuewen YangDongyan WangZhuoran YanShuwen ZhangMDPI AGarticleurban functional zoneU-Netspatial distributionKuancheng DistrictAgricultureSENLand, Vol 10, Iss 1266, p 1266 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	urban functional zone U-Net spatial distribution Kuancheng District Agriculture S
spellingShingle	urban functional zone U-Net spatial distribution Kuancheng District Agriculture S Yuewen Yang Dongyan Wang Zhuoran Yan Shuwen Zhang Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China
description	Scientific functional zone planning is the key to achieving long-term development goals for cities. The rapid development of remote sensing technology allows for the identification of urban functional zones, which is important since they serve as basic spatial units for urban planning and functioning. The accuracy of three methods—kernel density estimation, term frequency-inverse document frequency, and deep learning—for detecting urban functional zones was investigated using the Gaode points of interest, high-resolution satellite images, and OpenStreetMap. Kuancheng District was divided into twenty-one functional types (five single functional types and twenty mixed ones). The results showed that an approach using deep learning had a higher accuracy than the other two methods for delineating four out of five functions (excluding the commercial function) when compared with a field survey. The field survey showed that Kuancheng District was progressing towards completing the goals of the Land-Use Plan of the Central City of Changchun (2011–2020). Based on these findings, we illustrate the feasibility of identifying urban functional areas and lay out a framework for transforming them. Our results can guide the adjustment of the urban spatial structure and provide a reference basis for the scientific and reasonable development of urban land-use planning.
format	article
author	Yuewen Yang Dongyan Wang Zhuoran Yan Shuwen Zhang
author_facet	Yuewen Yang Dongyan Wang Zhuoran Yan Shuwen Zhang
author_sort	Yuewen Yang
title	Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China
title_short	Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China
title_full	Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China
title_fullStr	Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China
title_full_unstemmed	Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China
title_sort	delineating urban functional zones using u-net deep learning: case study of kuancheng district, changchun, china
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/913d461866ad4b7384ae17a095756c78
work_keys_str_mv	AT yuewenyang delineatingurbanfunctionalzonesusingunetdeeplearningcasestudyofkuanchengdistrictchangchunchina AT dongyanwang delineatingurbanfunctionalzonesusingunetdeeplearningcasestudyofkuanchengdistrictchangchunchina AT zhuoranyan delineatingurbanfunctionalzonesusingunetdeeplearningcasestudyofkuanchengdistrictchangchunchina AT shuwenzhang delineatingurbanfunctionalzonesusingunetdeeplearningcasestudyofkuanchengdistrictchangchunchina
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Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China

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