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作 者:吕笑飞 焦新颖 武文智 尚国琲 马景涛 王鹏 LV Xiao-fei;JIAO Xin-ying;WU Wen-zhi;SHANG Guo-fei;MA Jing-tao;WANG Peng(Hebei GEO University,Shijiazhuang 050031;Institute of Geographic Sciences and Natural Resources Research,CAS,Beijing 100101,China;Hebei Lingjian Spatial Planning and Consulting Co.,Ltd,Shijiazhuang 050031;Natural Resource Asset Capital Research Center,Heibei GEO University,Shijiazhuang 050031)
机构地区:[1]河北地质大学土地科学与空间规划学院,河北石家庄050031 [2]中国科学院地理科学与资源研究所,北京100101 [3]河北瓴建国土规划咨询有限公司,河北石家庄050031 [4]河北地质大学自然资源资产资本研究中心,河北石家庄050031
出 处:《河北地质大学学报》2022年第1期112-119,共8页Journal of Hebei Geo University
基 金:河北省社会科学基金项目(HB18YJ011)。
摘 要:采用深度学习技术SegNet网络模型对石家庄市27124张街景图像中的街道绿色特征进行提取,计算街道绿视率,在此基础上建立特征价格模型,进一步分析街道绿化对住宅价格的影响。研究结果表明:石家庄市街道整体绿视率平均值为25.92%,高于人眼感觉最舒适的街道绿化水平;街道绿视率空间分布差异较大,商场密集路段以及高架桥遮挡视线路段分值较低,城市支路分值普遍较高;街道绿视率对住宅价格影响程度较高,街道绿视率每提高1%,每套住宅价格平均上涨1.194万元。研究结果可为政府实施城市精细化管理提供帮助,为城市规划以及房地产开发提供依据。The SegNet network model,a deep learning technique,was used to extract street green features from 27,124 street images in Shijiazhuang city and calculate the street green view rate.Based on the feature price model,the impact of street greening on residential prices was further analyzed.The results show that:the overall street green view rate is 25.92%on average,which is higher than the most comfortable level of street greening for human eyes;the spatial distribution of street green view rate varies greatly,with dense sections of shopping malls and elevated bridges blocking the view easily forming low values,and urban feeder roads being the sections with high values;the street green view rate has a high degree of influence on residential prices,and for every 1%increase in street green view rate,residential prices increase by 1.194.The results of the study can help the government to implement urban refinement management and provide a basis for urban planning and real estate development.
关 键 词:街道绿视率 百度街景 特征价格模型 住宅价格 石家庄
分 类 号:K902[历史地理—人文地理学]
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