Large-Scale and Refined Green Space Identification-Based Sustainable Urban Renewal Mode Assessment

Abstract

Urban sustainable renewal has received extensive attention in a wide range of fields, including urban planning, urban management, energy management, and transportation. Given that environmental resource conservation is critical to urban sustainability renewal, this study highlighted the imbalance among green space, urban development, and transportation accessibility. Here, a novel node-place-green model is presented to measure sustainable urban development; meanwhile, deep learning is utilized to identify and extract the green space to measure the environmental index. Based on the generated node, place, and green value, urban developing status could be classified into nine modes for further analysis of transportation, urban function, and ecological construction. The experimental results of Harbin reveal the feasibility of the proposed method in providing specific guidelines for urban planning and policies on sustainable development.