Yu Mingyang, Chen Xin, Zheng Xiangyu, Cui Weikang, Ji Qingrui, Xing Huaqiao
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China.
Inspur Smart City Technology Co., Ltd, Jinan, 250014, China.
Sci Rep. 2025 May 26;15(1):18439. doi: 10.1038/s41598-025-03189-z.
Street visual quality improvement plays an important role in urban development. An important direction for street quality research lies in accurately perceiving the spatial quality of urban streets and exploring the connection with street constituents. This study applies deep learning to extract visual elements from street view images and uses a human-machine adversarial model to rate them across six dimensions (beautiful, wealthy, safety, lively, depressing, and boring). Through spatial visualization of street quality, overlay analysis with network accessibility, and multiple linear regression, it examines the correlations between street space quality and its constituent elements. The results indicate that the streets within the study area scored highly on the dimensions of beautiful and lively, this is attributed to the reasonable greening construction which characterized by the good layout and density of greenery. Such greening not only enhances aesthetics but also provides environmental benefits. Additionally, the orderly street layout reflects well-organized spatial arrangements of street elements, such as pathways and building facades. Positive visual perception such as beautiful, wealthy, safety, lively is positively correlated with plants and pedestrians, and negatively correlated with walls. It is important to address the distinct types of streets in urban planning, including high-quality/accessibility streets found in urban centers, high-quality/low-accessibility streets at district junctions with sparse networks, low-quality/high-accessibility streets in the southwestern center and Low-quality/accessibility peripheral areas characterized by outdated buildings. Strategies should prioritize improvements in street function, greening, building interfaces, and pedestrian connectivity. These measures will help enhance the overall spatial quality and vitality of the area. In summary, the findings provide data support for more precise urban street improvements and offer a reference for human-centered urban planning research.
街道视觉质量提升在城市发展中起着重要作用。街道质量研究的一个重要方向在于准确感知城市街道的空间质量，并探索其与街道构成要素的联系。本研究应用深度学习从街景图像中提取视觉元素，并使用人机对抗模型从六个维度（美观、富裕、安全、活力、压抑和乏味）对其进行评分。通过街道质量的空间可视化、与网络可达性的叠加分析以及多元线性回归，研究街道空间质量与其构成要素之间的相关性。结果表明，研究区域内的街道在美观和活力维度上得分较高，这归因于合理的绿化建设，其特点是绿化布局和密度良好。这种绿化不仅提升了美观度，还带来了环境效益。此外，有序的街道布局反映了街道要素（如道路和建筑立面）组织良好的空间安排。美观、富裕、安全、活力等积极的视觉感知与植物和行人呈正相关，与墙壁呈负相关。在城市规划中应对不同类型的街道加以关注，包括城市中心的高质量/高可达性街道、网络稀疏的区域交界处的高质量/低可达性街道、西南部中心的低质量/高可达性街道以及以老旧建筑为特征的低质量/低可达性边缘区域。策略应优先改善街道功能、绿化、建筑界面和行人连通性。这些措施将有助于提升该区域的整体空间质量和活力。总之，研究结果为更精确的城市街道改善提供了数据支持，并为以人为本的城市规划研究提供了参考。
