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

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.