The application discloses a kind of green
space network optimization method and
system for urban shrinkage, it is related to
landscape architecture, urban and rural planning technical field, through high-resolution
remote sensing image identification vacant land and combining
machine learning
algorithm establishes vacant land identification model, analysis vacancy rate and adopts K-means clustering division grid
unit type, constructs vacant land conversion
potential evaluation database and determines index weight using objective weighting method.Green
space network is constructed based on
graph theory model and the connection of node is analyzed using gravity model.Setting three target preference scenarios of life,
ecology and production, and simulating
network performance changes through global efficiency to determine the optimization threshold, finally combining
network topology structure index and potential value, the importance and conversion priority of node are divided.It can scientifically evaluate the potential of green space in the process of urban shrinkage, optimize the spatial
layout, improve the overall performance of green
space network, and provide scientific support for the planning and management of green space.