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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Bing Xiao & Xuexiu Jia & Dong Yang & Lingwen Sun & Feng Shi & Qitong Wang & Yongfei Jia, 2022. "Research on Classification Method of Building Function Oriented to Urban Building Stock Management," Sustainability, MDPI, vol. 14(10), pages 1-13, May.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5871-:d:814211
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    References listed on IDEAS

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    Cited by:

    1. Abdulkadir Memduhoglu & Melih Basaraner, 2024. "Semantic enrichment of building functions through geospatial data integration and ontological inference," Environment and Planning B, , vol. 51(4), pages 923-938, May.

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