利用生成式AI进行街景分析（SAGAI）：基于视觉-语言评估和城市场景制图
Streetscape Analysis with Generative AI (SAGAI): Vision-Language Assessment and Mapping of Urban Scenes
AI总结：
本文提出SAGAI，一种利用开放数据和视觉-语言模型分析街景的模块化流程，通过定制提示生成空间指标，实现城市场景的自动化制图与评估，展示了其在城市研究中的广泛应用潜力。
摘要：
街景是城市空间的重要组成部分。目前，其评估要么局限于其质量骨架的形态学属性，要么需要耗费大量人力进行视觉感知质量的定性评估。本文介绍了SAGAI：利用生成式人工智能进行街景分析，一种用于使用开放数据和视觉-语言模型对街道级城市场景进行评分的模块化流程。SAGAI整合了OpenStreetMap的几何数据、谷歌街景图像以及轻量级的LLaVA模型版本，通过可定制的自然语言提示从图像中生成结构化空间指标。该流程包括一个自动制图模块，能够汇总在点和街道层面的视觉评分，实现直接的制图解释。它无需特定任务的训练或专有软件依赖，支持对城市环境进行可扩展和可解释的分析。在尼斯和维也纳的两个探索性案例研究中，展示了SAGAI能够从视觉-语言推断中生成地理空间输出的能力。初步结果表明，SAGAI在二元城市-乡村场景分类中表现强劲，在商业特征检测中精度中等，在人行道宽度估计中结果较低，但仍然具有信息量。SAGAI可由任何用户完全部署，并通过仅修改提示即可轻松适应广泛的城市研究主题，如步行性、安全性或城市设计。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
