从街景图像到街道质量指标：面向郊区15分钟城市的视觉语言推理
From Street View Imagery to Street Quality Indicators: Vision Language Inference for the Suburban 15-minute City
AI总结：
本文以法国尼斯东北部郊区为研究对象，采用开源的SAGAI工作流，利用视觉语言模型从街景图像评估街景质量，发现理想街景仅存在于部分郊区区域，证明了VLM可支持郊区城市诊断，助力循证规划。
AI中文摘要：
街景质量已成为当代城市规划的核心关注点，尤其在以人为本的15分钟城市框架内，步行可达性与公共空间质量愈发被视为城市绩效的关键决定因素。然而，由于传统实地调查的时间和资源需求，对广阔郊区及城市边缘地带的街景质量进行评估仍具挑战性。本文针对法国尼斯东北部郊区的街景质量开展了面向规划的评估，采用了最新版本的SAGAI（基于生成式AI的街景分析），这是一款利用视觉语言模型（VLM）从谷歌街景图像进行大规模街景分析的开源工作流。新版本通过改进图像获取、生成地理一致的视图、支持多种VLM架构、基于共识的推理以及集成分析环境，解决了原始框架的局限性。该工作流被应用于数千条街道层面的观测，以评估与以人为本城市环境相关的质量：人行道存在性、行人入口密度及植被情况。生成的地图显示，理想的街景质量仅存在于当前郊区街景的一小部分区域，主要集中在紧凑型开发项目和传统郊区街区，而在住宅丘陵地带尤为匮乏。该分析证明了当代VLM在广阔郊区地区支持城市诊断的潜力，这些地区的实地调查将耗时过长。除案例研究外，本文还阐明了视觉语言模型的最新进展如何通过实现对城市公共空间质量的可扩展、灵活且可解释的评估，为循证规划做出贡献。
英文摘要：
Streetscape quality has become a central concern in contemporary urban planning, particularly within the framework of the pedestrian-friendly 15-minute city, where walkability and public-space quality are increasingly recognized as key determinants of urban performance. However, assessing streetscape qualities across large suburban and peri-urban territories remains challenging due to the time and resource demands of conventional field surveys. This paper presents a planning-oriented assessment of streetscape qualities in the north-eastern periphery of Nice (France) using the latest release of SAGAI (Streetscape Analysis with Generative AI), an open-source workflow that leverages vision-language models (VLMs) for large-scale streetscape analysis from Google Street View imagery. The new release addresses limitations of the original framework through improved image acquisition, geographically consistent view generation, support for multiple VLM architectures, consensus-based inference, and an integrated analytical environment. The workflow is applied to several thousand street-level observations to evaluate qualities relevant to pedestrian-friendly urban environments: sidewalk presence, pedestrian entrance density, and vegetation. The resulting maps reveal that the desired streetscape qualities characterize only a fraction of today's suburban streetscapes, mainly in compact developments and traditional suburban faubourgs, while they are particularly lacking on residential hills. The analysis demonstrates the potential of contemporary VLMs to support urban diagnostics in extensive suburban territories where fieldwork would be prohibitively time-consuming. Beyond the case study, the paper illustrates how recent advances in vision-language models can contribute to evidence-based planning by enabling scalable, flexible, and interpretable assessments of urban public-space quality.
