摘要
在新时代国土空间总体规划背景下,客观科学地划定城镇开发边界是规划工作的基础,也是重点与难点之一。针对现有研究在数据选取、方法构建和结果分析上存在的问题,本研究基于“正向规划”和“反规划”理论,从自然环境、社会经济和政策导向的综合视角出发,依托多源数据融合驱动的深度学习算法,提出一种城镇开发边界自动划定方法,并以广州市花都区为实证案例,进行了城镇开发边界自动划定和影响因素分析,结果表明:(1)本研究提出的方法能自动划定城镇开发边界,结果更为客观;(2)城镇开发边界研究结果与规划成果在空间分布趋势上具有较高一致性,相比之下研究结果用地集约节约程度高,更符合未来用地发展要求;(3)城市发展是多方面因素综合作用的结果,其中交通和人口是影响城市发展的关键因子。综上,本研究提出的方法能客观科学地自动划定城镇开发边界,研究结果符合未来用地发展趋势,能给国土空间总体规划提供参考。
In the current context of spatial planning of national land,delineating the urban development boundary objectively and scientifically is a key and difficult task in planning work.However,most existing methods about the delineation of urban development boundary is existing some problems such as data selection,method build and result analysis.In view of natural environment,social economy and policy orientation,a method of delineating urban development boundary automatically was been proposing based on multi-source data fusion and deep learning.Furthermore,the proposed method has been used to delimit the urban development boundary of Huadu District,Guangzhou City and analysis of influencing factors.The results show that:1)This method can delimit the urban development boundary automatically;2)The model’s results are highly consistent with the planning results in terms of spatial distribution trend,with a high degree of land intensive and economical use,which is more in line with the requirements of future land development;3)Urban development is the result of a combination of multiple factors,among which transportation and population are the primary factors affecting the urban development.All in all,the proposed method can delimit the urban development boundary automatically,objectively and scientifically.What’s more,the proposed method’s results are in line with the future trend of land use development,thus can provide better guidance for China’s spatial planning of national land.
作者
刘星南
骆仁波
陈玲
周艺霖
廖琪
罗宏明
Liu Xingnan;Luo Renbo;Chen Ling;Zhou Yilin;Liao Qi;Luo Hongming(Guangdong Provincial Institute of Land Surveying&Planning,Guangzhou 510075,Guangdong,China;Key Laboratory of Earth Surface System and Human-Earth Relations,Ministry of Natural Resources of China,Guangzhou 510075,Guangdong,China;School of Geographical and Remote Science,Guangzhou University,Guangzhou 510006,Guangdong,China)
出处
《地理科学》
CSSCI
CSCD
北大核心
2024年第12期2073-2082,共10页
Scientia Geographica Sinica
基金
国家自然科学基金项目(42071443)
广东省科技创新战略专项资金攀登计划(pdjh2023a0405)
广东乡村地域系统野外科学观测研究站项目(2021B1212050026)
广东省自然资源厅科技项目(GDZRZYKJ2023008,GDZRZYKJ2024001)资助。
关键词
城镇开发边界
深度学习
多源数据融合
深度神经网络
地理探测器
urban development boundary
deep learning
multi-source data fusion
deep neural networks
geographic detector
