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The significance of urban function recognition has stimulated the need for multi-source geospatial data fusion, especially the fusion between remote sensing images and spatiotemporal big data. In previous studies, the natural correspondence across multi-source geospatial data has often been ignored in the description of one object, which would influence the performance of data fusion. Therefore, this study introduces the cross-correlations mechanism to achieve the natural correspondence by taking remote sensing images, point of interest (POI), and real-time social media users as an example. It proposes a new cross-correlations based functional urban land use (CC-FLU) model to infer urban functions. The presented model extracts physical and human semantic features from multi-source geospatial data, then maps them to their common subspaces to obtain their cross-correlations respectively. These semantic …