Large-Scale and Refined Green Space Identification-Based Sustainable Urban Renewal Mode Assessment
Large-Scale and Refined Green Space Identification-Based Sustainable Urban Renewal Mode Assessment
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DOI：
10.1155/2020/2043019
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发表时间：
2020-09
影响因子：
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通讯作者：
R. Guo;Xiaoya Song;Peiran Li;Guangming Wu;Zhiling Guo
中科院分区：
文献类型：
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作者：
R. Guo;Xiaoya Song;Peiran Li;Guangming Wu;Zhiling Guo
城市可持续更新在城市规划、城市管理、能源管理、交通运输等领域受到广泛关注。鉴于环境资源节约是城市可持续发展的关键，这项研究强调了绿地、城市发展和交通可达性之间的不平衡。提出了一种新的节点-场所-绿色模型来衡量城市的可持续发展，同时利用深度学习来识别和提取绿地来衡量环境指数。根据生成的节点、地点和绿地价值，将城市发展状况划分为交通、城市功能、生态建设等9种模式。哈尔滨市的实验结果表明，该方法在为城市规划和可持续发展政策提供具体指导方面具有可行性。
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.