Research on Classification Method of Building Function Oriented to Urban Building Stock Management

Bing Xiao, Xuexiu Jia, Dong Yang, Lingwen Sun, Feng Shi, Qitong Wang and Yongfei Jia
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Bing Xiao: Institute of Science and Technology for Development of Shandong, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
Xuexiu Jia: Sustainable Process Integration Laboratory-SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology-VUT Brno, Technická 2896/2, 616 69 Brno, Czech Republic
Dong Yang: Institute of Science and Technology for Development of Shandong, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
Lingwen Sun: Institute of Science and Technology for Development of Shandong, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
Feng Shi: School of Environmental Science and Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
Qitong Wang: The Eastern Route of South to North Water Diversion Shandong Main Line Co., Ltd., Jinan 250014, China
Yongfei Jia: Institute of Science and Technology for Development of Shandong, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China

Sustainability, 2022, vol. 14, issue 10, 1-13

Abstract: With the development of human society, the urban population and the urban building stock have been continuously increasing. Environmental issues such as greenhouse gases emissions, air pollution, and construction waste have gradually emerged. Due to the lack of an urban functional area database, it is very time-consuming to manually identify building functional areas. As a result, most of the current research on urban building functions are estimated at a large regional scale or only detailed calculations of individual buildings. The building functions classification method needs to be further improved. Based on the traditional methods, this paper proposes a building function classification method with higher recognition accuracy and is less time-consuming. The method is then applied to a certain area of Chaoyang District, Beijing, for validation and verification. The results show that the urban building function classification method in this paper has a recognition rate of 96.18%, an overall classification accuracy of 94.37%, and a kappa coefficient of 0.9089. The classification results are in good agreement with the virtual interpretation. In addition, automatic classification of building functions is implemented using ArcPy in ArcGIS, which significantly improves the classification efficiency.

Keywords: urban building stock; building function classification; POI data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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