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Information on the mixed use of buildings helps understand the status of mixed-use urban vertical land and assists in urban planning decisions. Although a few studies have focused on this topic, the methods they used are quite complex and require manual intervention in extracting different function patterns of buildings, while building recognition rates remain unsatisfying. In this paper, we propose a new method to infer the mixed use of buildings based on a tensor decomposition algorithm, which integrates information from both high-resolution remote sensing images and social sensing data. We selected the Tianhe District of Guangzhou, China to validate our method. The results show that the recognition rate of buildings can reach 98.67%, with an average recognition accuracy of 84%. Our study proves that the tensor decomposition algorithm can extract different function patterns of buildings unsupervised, while remote sensing data can provide key information for inferring building functions. The tensor decomposition-based method can serve as an effective and efficient way to infer the mixed use of buildings, which can achieve better results with simpler steps.

While a mixture of residential and non-residential uses in urban development has advantages in reducing transportation energy consumption and improving efficiency of land utilization, the patterns of energy consumption in mixed-use buildings are largely unknown. To understand associations between the built environment and energy consumption and to find effective strategies for energy saving, this study aims to examine how the gas and electricity energy consumption of mixed-use properties is influenced by the characteristics of the immediate surroundings of the building as well as by the building’s attributes. The sample for this study is 22,109 mixed-use buildings in Seoul, Korea and the main source of outcome is electricity and gas energy consumption data retrieved from the open system of building data in 2015 and 2016. The regression results showed that a higher proportion of non-residential uses in mixed-use buildings was positively associated with higher electricity consumption overall but that it reduced gas energy use during the winter. In particular, increased restaurant and service use significantly influenced electricity consumption in the buildings. With regard to surrounding built environment, higher impervious surfaces and dense development near the buildings increased the electricity consumption of the buildings but it reduced gas energy consumption. Our results imply that, through the mediating effects of UHIs, the built environment characteristics of immediate surroundings may have indirect effects on energy consumption in mixed-use buildings.