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

Yuewen Yang, Dongyan Wang, Zhuoran Yan and Shuwen Zhang
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Yuewen Yang: College of Earth Sciences, Jilin University, Changchun 130061, China
Dongyan Wang: College of Earth Sciences, Jilin University, Changchun 130061, China
Zhuoran Yan: College of Earth Sciences, Jilin University, Changchun 130061, China
Shuwen Zhang: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China

Land, 2021, vol. 10, issue 11, 1-21

Abstract: 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.

Keywords: urban functional zone; U-Net; spatial distribution; Kuancheng District (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
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