# From Street View Imagery to Street Quality Indicators: Vision Language Inference for the Suburban 15-minute City

arXiv: https://arxiv.org/abs/2608.20026 · HTML: https://mlsanity.com/paper/2608.20026/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Builds on (most cited first, 13 of 13)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [Beyond the Visible: Benchmarking Occlusion Perception in Multimodal Large Language Models](https://mlsanity.com/paper/2508.04059/) (2025)
- [CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials](https://mlsanity.com/paper/2201.02260/) (2022)
- [Streetscape Analysis with Generative AI (SAGAI): Vision-Language Assessment and Mapping of Urban Scenes](https://mlsanity.com/paper/2504.16538/) (2025)
- [Do Vision-Language Models See Urban Scenes as People Do? An Urban Perception Benchmark](https://mlsanity.com/paper/2509.14574/) (2025)
- [BuildingView: Constructing Urban Building Exteriors Databases with Street View Imagery and Multimodal Large Language Mode](https://mlsanity.com/paper/2409.19527/) (2024)
- [Interpretable Multimodal Framework for Human-Centered Street Assessment: Integrating Visual-Language Models for Perceptual Urban Diagnostics](https://mlsanity.com/paper/2506.05087/) (2025)
- [StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery](https://mlsanity.com/paper/2506.14670/) (2025)
- [Urban Complexity through Vision Intelligence: Variance, Gradients, and Correlations across Six Italian Cities](https://mlsanity.com/paper/2511.09258/) (2025)

## Abstract (arXiv)

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.