Abstract
Streetscapes are an essential component of urban space. Their assessment is presently either limited to morphometric properties of their mass skeleton or requires labor-intensive qualitative evaluations of visually perceived qualities. This paper introduces SAGAI: Streetscape Analysis with Generative Artificial Intelligence, a modular workflow for scoring street-level urban scenes using open-access data and vision-language models. SAGAI integrates OpenStreetMap geometries, Google Street View imagery, and a lightweight version of the LLaVA model to generate structured spatial indicators from images via customizable natural language prompts. The pipeline includes an automated mapping module that aggregates visual scores at both the point and street levels, enabling direct cartographic interpretation. It operates without task-specific training or proprietary software dependencies, supporting scalable and interpretable analysis of urban environments. Two exploratory case studies in Nice and Vienna illustrate SAGAI's capacity to produce geospatial outputs from vision-language inference. The initial results show strong performance for binary urban-rural scene classification, moderate precision in commercial feature detection, and lower estimates, but still informative, of sidewalk width. Fully deployable by any user, SAGAI can be easily adapted to a wide range of urban research themes, such as walkability, safety, or urban design, through prompt modification alone.
Abstract (translated)
街道景观是城市空间的重要组成部分。目前，对它们的评估要么局限于其大规模骨架的形态计量属性，要么需要进行费时费力的定性评价来衡量视觉感知的质量。本文介绍了SAGAI：一种使用开源数据和视觉语言模型进行街景分析的工作流程，旨在利用生成式人工智能为街道级别的城市场景评分。SAGAI集成了OpenStreetMap几何图形、Google Street View影像以及轻量级LLaVA模型的版本，通过可定制的自然语言提示从图像中生成结构化的空间指标。该工作流包括一个自动映射模块，可以分别在点和街区级别上聚合视觉得分，从而直接进行地图解释。SAGAI无需特定任务训练或专有软件依赖，支持城市环境的大规模且易于理解的分析。 通过尼斯和维也纳两个探索性案例研究，展示了SAGAI如何从视觉-语言推理中产生地理空间输出的能力。初步结果显示，在二元城市/农村场景分类方面表现强劲，商业特征检测方面的精度适中，人行道宽度估计较低但仍具信息价值。任何用户都可以完全部署SAGAI，并通过修改提示来轻松适应各种研究主题，如可步行性、安全性或城市设计等。
URL
https://arxiv.org/abs/2504.16538
