Multi-dimensional Model-driven Digital Twin for River Greening

Abstract. In this paper, for the problems of low efficiency, high cost, and difficult assessment in the traditional river greening maintenance and supervision mode, a digital twin system construction scheme for river greening based on multidimensional modeling and remote sensing technology is proposed. The system utilizes multidimensional data such as high-resolution remote sensing images, Sentinel-2 data, ground survey data and management information, and combines artificial intelligence techniques such as object-oriented classification and deep learning to realize the automated extraction of river greening information and the dynamic monitoring of green plant growth. Meanwhile, a greening survey APP and a greening maintenance supervision information system were developed to realize efficient integration of internal and external data and information sharing, and to provide decision support for river greening management through data visualization, statistical analysis and other functions. Taking Beijing LS River as an example, the research results show that the system can effectively improve the efficiency and precision of river greening maintenance and supervision, reduce the labor cost, provide technical support for the refined management of river greening, and has important application value and promotion significance.