Delineation of urban development boundary based on deep learning and multi-source data fusion:A case study of Huadu District,Guangzhou
In the current context of spatial planning of national land,delineating the urban development bound-ary objectively and scientifically is a key and difficult task in planning work.However,most existing methods about the delineation of urban development boundary is existing some problems such as data selection,method build and result analysis.In view of natural environment,social economy and policy orientation,a method of delineating urban development boundary automatically was been proposing based on multi-source data fusion and deep learning.Furthermore,the proposed method has been used to delimit the urban development bound-ary of Huadu District,Guangzhou City and analysis of influencing factors.The results show that:1)This meth-od can delimit the urban development boundary automatically;2)The model's results are highly consistent with the planning results in terms of spatial distribution trend,with a high degree of land intensive and econom-ical use,which is more in line with the requirements of future land development;3)Urban development is the result of a combination of multiple factors,among which transportation and population are the primary factors affecting the urban development.All in all,the proposed method can delimit the urban development boundary automatically,objectively and scientifically.What's more,the proposed method's results are in line with the fu-ture trend of land use development,thus can provide better guidance for China's spatial planning of national land.
urban development boundarydeep learningmulti-source data fusiondeep neural networksgeo-graphic detector
NETL
NSTL
万方数据
