Urbanization and urban expansion trigger the three-dimensional growth and replacement of buildings in the horizontal and vertical directions, accompanied by the use and consumption of a large number of materials and energy, forming the “metabolism” of urban buildings. The Institute of Urban Environment of the Chinese Academy of Sciences conducted research on the basis of the construction of urban building material metabolism model and the initial construction of urban building metabolism “simulator” (Liu et al.): the automation and intelligent inversion of key input parameters of the “simulator” part were realized, the description and simulation process of the building metabolic process was optimized, and the scope of application of the “simulator” was expanded (Xiamen to Shenzhen), laying a solid foundation for the further promotion and application of the “simulator”.
Aiming at the problem of automating and accurately obtaining building attribute information, the research uses machine learning to excavate the morphological characteristics of buildings at the individual level and the spatial structure of neighborhood-level( neighborhood-level), and develops the inversion method of key attributes of buildings. The results show that the prediction accuracy of the machine learning model for the key attribute information of the building (building age) reaches 89%, and the urban building form and geographical features play a key role in the model prediction process, which provides an example scenario and potential solution for automatic inversion and acquisition of other similar key parameters of the city.
The results were published in Frontiers in Earth Science. The research work is supported by the Strategic Leading Science and Technology Project of the Chinese Academy of Sciences, the International Cooperation Project, the Youth Innovation Promotion Association, and the National Natural Science Foundation of China.
Shenzhen construction data inversion, building material stock and flow simulation and mapping
At present, the urban environment has established a series of spatio-temporal analysis frameworks, methods and models around the theme of urban metabolism, which can support the simulation of high-precision spatio-temporal metabolic processes of urban buildings, home appliances and solid waste, and on this basis, a key step has been taken to the automation of key input parameters of the “simulator” through the above research. (Source: Institute of Urban Environment, Chinese Academy of Sciences)
Related paper information:https://doi.org/10.3389/feart.2022.944865
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