Streetscape Analysis with Generative AI (SAGAI): Vision-Language Assessment and Mapping of Urban Scenes

Streetscape Analysis with Generative AI (SAGAI): Vision-Language Assessment and Mapping of Urban Scenes Joan Perez 1 and Giovanni Fusco 2 1 Urban Geo Analytics, France 2 Universit e Cˆ ote-Azur-CNRS-AMU-Avignon Universit e, ESPACE, France April 2025 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. Keywords: Vision-Language Models, Street View Imagery, Streetscape Analysis, Geospatial AI, zero-shot inference 1 Introduction Assessing the qualities and functions of urban streetscapes is essential to understand walkability, safety, commercial vitality, and social life in cities [1, 2, 3]. However, traditional methods for observing and evaluating street-level conditions, such as field surveys, audits, and manual photo interpretation, remain time-consuming, labor-intensive, and difficult to scale beyond small pilot zones [2]. Geo-processing of vector models of the built environment allows the assessment of Email: jperez@urbangeoanalytics.com, ORCID: 0000-0003-3003-0895 Email: giovanni.fusco@univ-cotedazur.fr,