For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on single remote sensing image data or social sensing data. The multi-dimensional information which was attained from various data source and could reflect the attribute or function about the urban functional regions that could be lost in some extent. To sense urban functional regions comprehensively and accurately, we developed a multi-mode framework through the integration of spatial geographic characteristics of remote sensing images and the functional distribution characteristics of social sensing data of Point-of-Interest (POI). In this proposed framework, a deep multi-scale neural network was developed first for the functional recognition of remote sensing images in urban areas, which explored the geographic feature information implicated in remote sensing. Second, the POI function distribution was analyzed in different functional areas of the city, then the potential relationship between POI data categories and urban region functions was explored based on the distance metric. A new RPF module is further deployed to fuse the two characteristics in different dimensions and improve the identification performance of urban region functions. The experimental results demonstrated that the proposed method can efficiently achieve the accuracy of 82.14% in the recognition of functional regions. It showed the great usability of the proposed framework in the identification of urban functional regions and the potential to be applied in a wide range of areas.
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