Research on urban built up environment identification based on multi source spatial data fusion
Accurately identifying the built environment can provide scientific basis for the study of the interaction between urban spatial structure and urban transportation,and provide a data foundation for the rational allocation of urban spatial resources.Based on this,a deep learning model based on semantic recognition is investigated On the basis of fusing the basic analysis units of interest points and interest surfaces,high-dimensional semantic feature vectors of interest points within interest surfaces are extracted,and functional land types are identified through clustering analysis.The results show that the recognition accuracy of the deep learning model reaches 80%,and the model can provide new methodological ideas for built environment recognition..
NETL
NSTL
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