Identification and Analysis of Urban Functional Areas by Fusion of Multi-source Data
The rapid development of urbanization has changed the spatial structure of the city,and the rational division of urban functional areas is conducive to the monitoring of urbanization and ur-ban planning and management.Remote sensing images can reflect the physical characteristics of ground objects,but cannot obtain their socioeconomic characteristics.This study uses Gaofen-2 re-mote sensing image data,POI data,nighttime light data and building outline data to integrate the feature information of multi-source data and implement the division of urban functional areas based on the Scikit-Learn machine learning method.Firstly,the traffic analysis area was constructed with the road network as the basic research unit,and the research area was divided into 827 plots,and then combined with kernel density analysis,frequency density method and regional analysis,extrac-ted and the feature information of multi-source data is integrated to identify urban functional areas based on three classification models.The research results show that the best recognition results are achieved by comprehensively utilizing the BOVW model constructed from spectrum and texture,so-cioeconomic characteristics constructed from POI and night light data,and landscape characteristics of building outline data,combined with the random forest model method,its accuracy is as high as 76.65%.The feasibility and effectiveness of this method are verified.
Gaofen-2 remote sensing imagePOIrandom foresturban functional area
国家科技期刊平台
NSTL
万方数据
维普
