• Journal of Geo-information Science
  • Vol. 22, Issue 6, 1330 (2020)
Cheng CUI1、1、2、2, Hongyan REN1、1、*, Lu ZHAO1、1、2、2, and Dafang ZHUANG1、1
Author Affiliations
  • 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
  • 1State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2. 中国科学院大学资源与环境学院,北京 100190
  • 2College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China
  • show less
    DOI: 10.12082/dqxxkx.2020.200072 Cite this Article
    Cheng CUI, Hongyan REN, Lu ZHAO, Dafang ZHUANG. Street Space Quality Evaluation in Yuexiu District of Guangzhou City based on Multi-feature Fusion of Street View Imagery[J]. Journal of Geo-information Science, 2020, 22(6): 1330 Copy Citation Text show less
    References

    [1] Building an image of villages-in-the-city: A clarification of China's distinct urban spaces[J]. International Journal of Urban and Regional Research, 34, 421-437(2010).

    [2] Slums from space-15 years of slum mapping using remote sensing[J]. Remote Sensing, 8, 455(2016).

    [3] 张丽英, 裴韬, 陈宜金, 等. 基于街景图像的城市环境评价研究综述[J]. 地球信息科学学报, 2019,21(1):46-58. [ Zhang LY, PeiT, Chen YJ, et al. A review of urban environmental assessment based on street view images[J]. Journal of Geo-information Science, 2019,21(1):46-58. ] [ Zhang L Y, Pei T, Chen Y J, et al. A review of urban environmental assessment based on street view images[J]. Journal of Geo-information Science, 2019,21(1):46-58. ]

    [4] Research on visual positioning based on urban street view. Beijing: Beijing University of Civil Engineering And Architecture(2018).

    [5] et al'Big data' for pedestrian volume: exploring the use of google street view images for pedestrian counts[J]. Applied Geography, 63, 337-345(2015).

    [6] et alAssessing street-level urban greenery using google street view and a modified green view index[J]. Urban Forestry & Urban Greening, 14, 675-685(2015).

    [7] et alStreetscore-predicting the perceived safety of one million streetscapes[C]. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, USA Columbus, 779-785(2014).

    [8] 周垠, 龙瀛. 街道步行指数的大规模评价——方法改进及其成都应用[J].上海城市规划,2017(1):88-93. [ ZhouY, LongY. Large-scale evaluation for street walkability: Methodological improvements and the empirical application in Chengdu[J]. Shanghai Urban Planing Review, 2017(1):88-93. ] [ Zhou Y, Long Y. Large-scale evaluation for street walkability: Methodological improvements and the empirical application in Chengdu[J]. Shanghai Urban Planing Review, 2017(1):88-93. ]

    [9] 唐婧娴, 龙瀛. 特大城市中心区街道空间品质的测度——以北京二三环和上海内环为例[J]. 规划师, 2017,33(2):68-73. [ Tang JX, LongY. Metropolitian street space quality evalution: second and third ring of Beijing, inner ring of Shanghai[J]. Planners, 2017,33(2):68-73. ] [ Tang J X, Long Y. Metropolitian street space quality evalution: second and third ring of Beijing, inner ring of Shanghai[J]. Planners, 2017,33(2):68-73. ]

    [10] Street space quality evaluation of historical and cultural streets based on street view map: Take Guangzhou as an example. Guangzhou: Guangzhou University(2019).

    [11] 叶宇, 张昭希, 张啸虎, 等. 人本尺度的街道空间品质测度——结合街景数据和新分析技术的大规模,高精度评价框架[J]. 国际城市规划, 2019,34(1):18-27. [ YeY, Zhang ZX, Zhang XH, et al. Human-scale quality on streets: A large-scale and efficient analytical approach based on street view images and new urban analytical tools[J]. Urban Planning International, 2019,34(1):18-27. ] [ Ye Y, Zhang Z X, Zhang X H, et al. Human-scale quality on streets: A large-scale and efficient analytical approach based on street view images and new urban analytical tools[J]. Urban Planning International, 2019,34(1):18-27. ]

    [12] et alA machine learning-based method for the large-scale evaluation of the qualities of the urban environment[J]. Computers, Environment and Urban Systems, 65, 113-125(2017).

    [13] 甘欣悦, 佘天唯, 龙瀛. 街道建成环境中的城市非正规性基于北京老城街景图片的人工打分与机器学习相结合的识别探索[J].时代建筑,2018(1):62-68. [ Gan XY, She TW, LongY. Understanding urban informality in street built environment: Combining manual evaluation with machine learning in processing the Beijing old city's street-view images[J]. Time Architecture, 2018(1):62-68. ] [ Gan X Y, She T W, Long Y. Understanding urban informality in street built environment: Combining manual evaluation with machine learning in processing the Beijing old city's street-view images[J]. Time Architecture, 2018(1):62-68. ]

    [14] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017,40(6):1229-1251. [ Zhou FY, Jin LP, DongJ. Review of convolutional neural network[J]. Chinese Journal of Computers, 2017,40(6):1229-1251. ] [ Zhou F Y, Jin L P, Dong J. Review of convolutional neural network[J]. Chinese Journal of Computers, 2017,40(6):1229-1251. ]

    [15] High-resolution remote sensing image retrieval based on fusion and pooling to transfer features from convolutional neural network. Nanchang: Nanchang University(2019).

    [16] 彭清, 季桂树, 谢林江, 等. 卷积神经网络在车辆识别中的应用[J]. 计算机科学与探索, 2018,12(2):282-291. [ PengQ, Ji GS, Xie LJ, et al. Application of convolution neural network in vehicle recognition[J]. Journal of Frontiers of Computer Science and Technology, 2018,12(2):282-291. ] [ Peng Q, Ji G S, Xie L J, et al. Application of convolution neural network in vehicle recognition[J]. Journal of Frontiers of Computer Science and Technology, 2018,12(2):282-291. ]

    [17] 黄冬梅, 刘佳佳, 苏诚, 等. 多特征融合的复杂环境海洋涡旋识别[J]. 中国图象图形学报, 2019,24(1):31-38. [ Huang DM, Liu JJ, SuC, et al. Ocean eddies recognition based on multi-features fusion in complex environment[J]. Journal of Image and Graphics, 2019,24(1):31-38. ] [ Huang D M, Liu J J, Su C, et al. Ocean eddies recognition based on multi-features fusion in complex environment[J]. Journal of Image and Graphics, 2019,24(1):31-38. ]

    [18] Surf: Speeded up robust features[C]. Papers of the European Conference on Computer Vision, Austria Graz, 404-417(2006).

    [19] Histograms of oriented gradients for human detection[C]. Papers of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, USA San Diego, 886-893(2005).

    [20] et al[online]. Enet: A deep neural network architecture for real-time semantic segmentation. https://arxiv.org/abs/1606.02147

    [21] 陆波, 尉询楷, 毕笃彦. 支持向量机在分类中的应用[J]. 中国图象图形学报, 2005,10(8):1029-1035. [ LuB, Wei XK, Bi DY. Application of support vector machine in classification[J]. Journal of Image and Graphics, 2005,10(8):1029-1035. ] [ Lu B, Wei X K, Bi D Y. Application of support vector machine in classification[J]. Journal of Image and Graphics, 2005,10(8):1029-1035. ]

    [22] 周超, 方秀琴, 吴小君, 等. 基于三种机器学习算法的山洪灾害风险评价[J]. 地球信息科学学报, 2019,21(11):1679-1688. [ ZhouC, Fang XQ, Wu XJ, et al. Risk assessment of mountain torrents based on three machine learning algorithms[J]. Journal of Geo-information Science, 2019,21(11):1679-1688. ] [ Zhou C, Fang X Q, Wu X J, et al. Risk assessment of mountain torrents based on three machine learning algorithms[J]. Journal of Geo-information Science, 2019,21(11):1679-1688. ]

    [23] 黄衍, 查伟雄. 随机森林与支持向量机分类性能比较[J]. 软件, 2012,33(6):111-114. [ HuangY, Zha WX. Comparison on classification performance between random forests and support vector machine[J]. Computer Engineering & Software, 2012,33(6):111-114. ] [ Huang Y, Zha W X. Comparison on classification performance between random forests and support vector machine[J]. Computer Engineering & Software, 2012,33(6):111-114. ]

    [24] A bayesian hierarchical model for learning natural scene categories[C]. Papers of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, USA San Diego, 524-531(2005).

    [25] Building block level urban land-use information retrieval based on google street view images[J]. GIScience & Remote Sensing, 54, 819-835(2017).

    [26] et alUse of tencent street view imagery for visual perception of streets[J]. ISPRS International Journal of Geo-information, 6, 265(2017).

    Cheng CUI, Hongyan REN, Lu ZHAO, Dafang ZHUANG. Street Space Quality Evaluation in Yuexiu District of Guangzhou City based on Multi-feature Fusion of Street View Imagery[J]. Journal of Geo-information Science, 2020, 22(6): 1330
    Download Citation