A novel semantic recognition framework of urban functional zones supporting urban land structure analytics based on open‐source data

Abstract To solve problems related to urban land development, protection, and management, urban planners focus on achieving high precision and currency for fine‐grained semantic recognition of urban functional zones (UFZs). The classification of discrete functional zones and qualitative recognition of UFZs have been well studied, but the precise recognition of UFZs with explicit depictions of socioeconomic urban functions has seldom been addressed. Thus, we propose a novel open‐source data‐guided semantic urban function recognition (OSD‐SFR) framework to analyze UFZs and quantitatively link open‐source data (OSD) with the Chinese national urban function classification code. The spatial relationship between the combined semantic database and the segmented urban zones data set was measured, graded, and categorized. Kernel density and statistical methods based on a single data source of points‐of interest were used for comparative experiments. The results indicate that OSD‐SFR yields more fine‐grained delineations between urban functions and achieves more accurate recognition. Critical semantic information from different OSD sources is deeply integrated. This study provides a realistic reference, including detailed descriptions of the spatial distribution and morphological structure of UFZs, to support long‐term city planning.