Abstract
Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.
Highlights
Urbanization in developing countries has progressed rapidly over the past 20 years [1]
Our analysis showed that urban functional zones could be extracted from high-resolution remote sensing imagery with the aid of points of interest (POIs) with reasonable accuracy of 78.47%
Our results show that the high-resolution multi-spectral imagery, like GF-2 with 1-meter spatial resolution, allow the accurate segmentation of different building roofs and provide accurate building rooftop polygon for combining with POIs
Summary
Urbanization in developing countries has progressed rapidly over the past 20 years [1]. The availability of multispectral satellite remote imagery and big data, such as information on traffic and the utilization of cellular mobile phone networks, provides great potential in mapping urban functional zones [10]. Training samples based on the spatial arrangement of buildings (segments or objects) are restricted by the different types of roofs or architecture styles across cities [17] Such approaches often provide limited model training information for urban functional zone on a broader scale. Mapping urban land use with remotely sensed imagery is more difficult in built-up areas because most remotely-sensed imagery merely records the natural characteristics of land cover, which can be associated with electromagnetic reflectance, and further assessed with texture measures or grouped into broad classes, such as “forest” or “built up” This poses a deficiency in information about human and social activities. The segmentation and parcels in a built-up area were used as the input dataset for functional calculations
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