基于深度学习与多源数据融合的城镇开发边界划定——以广州市花都区为例
Delineation of urban development boundary based on deep learning and multi-source data fusion:A case study of Huadu District,Guangzhou
刘星南 1骆仁波 2陈玲 1周艺霖 1廖琪 1罗宏明1
作者信息
- 1. 广东省土地调查规划院,广东 广州 510075;自然资源部陆表系统与人地关系重点实验室,广东 广州 510075
- 2. 广州大学地理科学与遥感学院,广东 广州 510006
- 折叠
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.
Abstract
In the current context of spatial planning of national land,delineating the urban development bound-ary 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 bound-ary of Huadu District,Guangzhou City and analysis of influencing factors.The results show that:1)This meth-od 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 econom-ical 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 fu-ture trend of land use development,thus can provide better guidance for China's spatial planning of national land.
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Key words
urban development boundary/deep learning/multi-source data fusion/deep neural networks/geo-graphic detector引用本文复制引用
出版年
2024
NETL
NSTL
万方数据