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基于街景的街道空间品质测度方法完善及示例研究 被引量:24

The Improvement of Street Space Quality Measurement Method Based on Streetscape
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摘要 为进一步深化已有的基于街景街道空间品质的测度方法,增加其准确性与应用可行性,文章对其实际应用过程进行完善:针对我国现有街区尺度制定样本选取规则,根据人的视野规律增加图片采集角度,并增加自动数据采集与分析方法。在此基础上,将完善后的方法应用于厦门市中山路片区与火车站片区的街道空间品质测度及对比研究中,发现两片区街道空间品质存在的共性与特性问题,并提出相应的改善建议。 To improve the measurement method of street space quality in its accuracy and feasibility,the paper improves its process of application: First,the sample selection rules is formulated according to the existing street scales in China;Second,the angles of picture collection are increased based on the law of human vision;and automatic data acquisition and analysis methods are added. Finally,the improved method was applied to the measurement and comparison of street space quality in the Zhongshan Road area and Railway Station area of Xiamencity.Further improvement suggestions are raised with respect to common and particular problems of street space quality.
作者 戴智妹 华晨 Dai Zhimei;Hua Chen
出处 《规划师》 北大核心 2019年第9期57-63,共7页 Planners
关键词 街道空间 空间品质 测度方法 厦门市 Street space Space quality Measurement method Xiamen
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  • 1周进,黄建中.城市公共空间品质评价指标体系的探讨[J].建筑师,2003,0(3):52-56. 被引量:59
  • 2范今朝,黄吉燕.城市地名规划及命名规则[J].城市问题,2005(1):2-5. 被引量:25
  • 3王际桐.中国汉语地名通名的规范[J].中国地名,2002(3):20-23. 被引量:10
  • 4Long Y. Redefining Chinese city system with open data. Beijing City Lab, 2016.
  • 5Long Y, Liu L. 2016. How green are streets? An analysis on Tencent street view in 245 major Chinese cities[D]. Beijing City Lab, 2016.
  • 6Liu, X, Long, Y. Automated Identification and Characterization of Parcels with OpenStreetMap and Points of Interest [EB/OL]. Environment and Planning B: Planning & Design, http://epb. sagepub.com/content/early/2015/09/02/0265813515604767. fulLpdf+html, 2015-09-02.
  • 7KRIZHEVSKYK A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networksFC~ //Advances in Neural Information Processing Systems. Red Hook, NY:Curran Associates Press, 2012:1097-1105.
  • 8DENG J ,DONG W, SOCHER R, et al. ImageNet: a large-scale image databaseEC~//IEEE Conference on Computer Vi- sion and Pattern Recognition. Miami: IEEE Press, 2009 : 248-255.
  • 9RUMELHART D E, HINTON G E,WILLIAMS R J. Learning representations by back-propagating errorsEJ]. Nature, 1986,323:533-536.
  • 10GLOROT X,BORDES A, BENGIO Y S. Deep sparse rectifier neural networks[J]. Journal of Machine Learning Re- search,2010(16) :315-323.

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