Oct 2026· Street Art & Urban Creativity· Vol 12, pp. 691-708· 0 citations
Urban Design and Spatial Analysis
TL;DR
A scalable, privacy-preserving vision-language model (VLM) pipeline is developed that leverages zero-shot CLIP to extract 150+ interpretable urban design features from street-level imagery and enables scalable, evidence-based urban planning across cities.
Abstract
Municipalities need current, granular built-environment data to support effective planning. However, current collection methods are expensive, challenging to scale, and present privacy concerns. To address this gap, the authors collaborated with the U.S. Department of Transportation (USDOT) through a Small Business Innovation Research (SBIR) initiative. We developed a scalable, privacy-preserving vision-language model (VLM) pipeline that leverages zero-shot CLIP to extract 150+ interpretable urban design features from street-level imagery. Applied to Philadelphia’s bus network, the pipeline processed over two million images and generated block-level predictions for more than 8,000 blocks. This approach enables scalable, evidence-based urban planning across cities.
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