摘要
随着当前城市规划与设计的精细化转型,人本尺度下的街道空间品质特征研究日益受到广泛关注。作为影响街道空间品质的关键要素之一,街道渗透率即街道底层门窗洞口面积占底层界面面积的比例的量化测度需求日益提升。现有渗透率的测度主要依赖于成本高、效率低的手工分析,难以进行大规模、高效地测度。针对这一问题,本研究基于开源街景数据和机器学习算法,提出了一套人本视角下街道渗透率大规模、精细化测度和分析方法,并以上海中心城区为例,快速高效地实现了该范围内街道渗透率的计算和可视化。人工标注的结果与计算机的智能化识别在校核中显示了较高的拟合度,证明了该方法的有效性。实证分析发现,上海中心城区内街道渗透率存在明显的空间异质性,呈现“内高外低”的空间格局。本研究对经典城市设计要素与新数据新技术的深度整合方法可为人本导向的城市设计实践提供有力支持,同时兼具大规模与高精度的宏观图解也有助于提高设计师对街道空间的深入认知。
Accompanying with the delicacy transformation of urban planning and design,humanscale street qualities have been regarded as key issues in recent years.Street transparency refers to the percentage of the area of the street door and window openings to the area of the street interface.Nevertheless,existed studies mainly rely on manual-based analytical approaches with high-cost and lowefficiency,which is hard to be measured on the city scale.As a response to this issue,this paper proposes a set of large-scale and refined measurement and analysis methods for street transparency based on the integration of street view images and deep learning.Taking Shanghai as an example,this paper completes the calculation and visualization of street interface transparency effectively.The verification via manual labeling obtains high coefficients with automatically computed results in statistical analysis,which proves the validity of this study.The empirical analysis finds that there is obvious spatial heterogeneity in street transparency in the central Shanghai,showing a spatial pattern of“high inside and low outside”.This paper is a result of integrating classical urban design concerns with new data and new techniques,which helps support human-oriented urban design practice;and it reveals a big picture co-presenting large-scale and high-precision results,which helps designers to seek in-depth understandings in this direction.
作者
邵源
叶丹
叶宇
Shao Yuan;Ye Dan;Ye Yu
出处
《国际城市规划》
CSSCI
CSCD
北大核心
2023年第6期39-47,共9页
Urban Planning International
基金
国家自然科学基金面上项目(52078343)
国家重点研发计划(2023YFC3805503)。
关键词
城市设计
街道
界面渗透率
深度学习
街景数据
人本尺度
Urban Design
Street
Interface Transparency
Deep Learning
Street View Image
Human-scale
