基于机器学习的街道空间品质测量与规划

张 萌
澳门城市大学创新设计学院

摘要


街道空间作为由建筑、绿植、道路等要素构成的公共空间，其质量对城市发展具有重要促进作用。传统研究受限于数据获取难度及传统调查方法的繁琐性，导致大规模街道空间品质的量化分析存在周期长、精度低等问题。本研究提出基于数据驱动的街道空间品质评估方法，通过百万级规模的Place Pulse街景图像情感感知数据集训练深度学习模型，构建城市街道空间情感感知的预测与可视化系统。为实现主客观数据的融合分析，采用语义分割技术提取街景图像中18类视觉要素的空间占比特征，并通过相关性检验探究其与情感感知评分的关联机制。该方法不仅为街道空间感知模式研究提供创新技术路径，更为城市街道规划中的情感化设计策略制定提供科学依据。

关键词


深度学习；城市规划；情感感知；街景图像

全文:

PDF


参考


[1]葛岩，& 唐雯.（2017）.城市街道设计导则的编制探索——以《上海市街道设计导则》为例.上海城市规划，01，9-16.

[2]梁景宇.（2020）.基于多源数据的澳门城市街道空间品质探索（硕士学位论文，华南理工大学）. https：//doi.org/10.27151/d.cnki.ghnlu.2020.001686

[3]曾佳.（2013）.城市慢行交通空间人性化设计研究（硕士学位论文，北京建筑大学）. http：//106.52.93.171：8085/kcms2/article/abstract?v=64ENavj7QCCTiXDumdVkQOf_w8qw8PBR2xCzP-fgf4R4NWUv-NcDaxQ3qXbuGsjuz1H6lhLXxH-DqML_hLLjDb06taBohSLNjLIGxwY7RdAbVNbs7mDQAroEHhfyMrIIDef1G3__1eH42uc-NSn60arRuxoJwubXU-vjTqaExsmKbcycax3ZmxlzX7UjkFhxqjbB8AxD5Ho=&uniplatform=NZKPT&language=CHS

[4]Jacobs, J. (2012). The death and life of great American cities. Vintage Books.

[5]Xia, C., Zhang, A., & Yeh, A. G. O. (2021). The varying relationships between multidimensional urban form and urban vitality in Chinese megacities: Insights from a comparative analysis. Annals of the American Association of Geographers, 112(1), 141-166. https://doi.org/10.1080/24694452.2021.1919502

[6]Sulis, P., Manley, E., Zhong, C., et al. (2017). Using mobility data as a proxy for measuring urban vitality. Journal of Spatial Information Science, 2018(16), 384.

[7]Ewing, R., & Handy, S. (2009). Measuring the unmeasurable: Urban design qualities related to walkability. Journal of Urban Design, 14(1), 65-84. https://doi.org/10.1080/13574800802451155

[8]Duan, J., Wang, Y., Fan, C., et al. (2018). Perception of urban environmental risks and the effects of urban green infrastructures (UGIs) on human well-being in four public green spaces of Guangzhou, China. Environmental Management, 62, 500-517. https://doi.org/10.1007/s00267-018-1068-8

[9]Bonaiuto, M. (2004). Residential satisfaction and perceived urban quality. In C. D. Spielberger (Ed.), Encyclopedia of applied psychology (pp. 267-272). Elsevier. https://doi.org/10.1016/B0-12-657410-3/00698-X

[10]Liu, M., Han, L., Xiong, S., Qing, L., Ji, H., & Peng, Y. (2019). Large-scale street space quality evaluation based on deep learning over street view image. In Y. Zhao, N. Barnes, B. Chen, R. Westermann, X. Kong, & C. Lin (Eds.), Image and graphics. ICIG 2019. Lecture notes in computer science (Vol. 11902, pp. 579-588). Springer, Cham. https://doi.org/10.1007/978-3-030-34110-7_58

[11]Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018). Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning, 180, 148-160. https://doi.org/10.1016/j.landurbplan.2018.08.020

[12]Ordonez, V., Berg, T.L. (2014). Learning High-Level Judgments of Urban Perception. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision - ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8694. Springer, Cham. https://doi.org/10.1007/978-3-319-10599-4_32

[13]Salesses, P., Schechtner, K., & Hidalgo, C. A. (2013). The collaborative image of the city: Mapping the inequality of urban perception. PLoS ONE, 8(7), e68400. https://doi.org/10.1371/journal.pone.0068400

[14]Porzi, L., Rota Bulò, S., Lepri, B., & Ricci, E. (2015). Predicting and understanding urban perception with convolutional neural networks. In Proceedings of the 23rd ACM international conference on Multimedia (MM '15) (pp. 139-148). Association for Computing Machinery. https://doi.org/10.1145/2733373.2806273

[15]龙瀛，& 唐婧娴.（2019）.城市街道空间品质大规模量化测度研究进展.城市规划，06，107-114.

[16]李智，& 龙瀛.（2018）.基于动态街景图片识别的收缩城市街道空间品质变化分析——以齐齐哈尔为例.城市建筑，06，21-25. https：//doi.org/10.19892/j.cnki.csjz.2018.06.004

[17]Li, X., Zhang, C., Li, W., Kuzovkina, Y. A., & Weiner, D. (2015). Who lives in greener neighborhoods? The distribution of street greenery and its association with residents’ socioeconomic conditions in Hartford, Connecticut, USA. Urban Forestry & Urban Greening, 14(4), 751-759. https://doi.org/10.1016/j.ufug.2015.07.007

[18]Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A. (2016). Deep learning the city: Quantifying urban perception at a global scale. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Computer Vision - ECCV 2016. ECCV 2016 Lecture Notes in Computer Science (Vol. 9905, pp. 451-466). Springer. https://doi.org/10.1007/978-3-319-46448-0_12

[19]McLeod, J. (Ed.). (2007). The Routledge Companion to Postcolonial Studies (1st ed.). Routledge. https://doi.org/10.4324/9780203358085

[20]Gong, Y., Palmer, S., Gallacher, J., Marsden, T., & Fone, D. (2016). A systematic review of the relationship between objective measurements of the urban environment and psychological distress. Environment International, 96, 48-57.https://doi.org/10.1016/j.envint.2016.08.019

[21]Yue, W., Chen, Y., Zhang, Q., & Liu, Y. (2019). Spatial explicit assessment of urban vitality using multi-source data: A case of Shanghai, China. Sustainability, 11(3), 638. https://doi.org/10.3390/su11030638

[22]Salesses, P., Schechtner, K., & Hidalgo, C. A. (2013). The collaborative image of the city: Mapping the inequality of urban perception. PLoS ONE, 8(7), e68400. https://doi.org/10.1371/journal.pone.0068400

[23]Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018). Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning, 180, 148-160. https://doi.org/10.1016/j.landurbplan.2018.08.020


Refbacks

  • 当前没有refback。