From Pixels to Urban Policy-Intelligence: Recovering Legacy Effects of Redlining with a Multimodal LLM

2025/09/18 by Anthony Howell, Nancy Wu, Howell, Anthony +5
Social Sciences · Environmental Science · #Human Mobility and Location-Based Analysis #Urban Transport and Accessibility #Land Use and Ecosystem Services

paper · pdf · doi:10.48550/arxiv.2509.15132

Abstract

This paper shows how a multimodal large language model (MLLM) can expand urban measurement capacity and support tracking of place-based policy interventions. Using a structured, reason-then-estimate pipeline on street-view imagery, GPT-4o infers neighborhood poverty and tree canopy, which we embed in a quasi-experimental design evaluating the legacy of 1930s redlining. GPT-4o recovers the expected adverse socio-environmental legacy effects of redlining, with estimates statistically indistinguishable from authoritative sources, and it outperforms a conventional pixel-based segmentation baseline-consistent with the idea that holistic scene reasoning extracts higher-order information beyond object counts alone. These results position MLLMs as policy-grade instruments for neighborhood measurement and motivate broader validation across policy-evaluation settings.

Citations

• Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations
• OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence
• StreetviewLLM: Extracting Geographic Information Using a Chain-of-Thought Multimodal Large Language Model
• Charting New Territories: Exploring the Geographic and Geospatial Capabilities of Multimodal LLMs
• On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence
• GPT-4 Technical Report
• Sensing urban soundscapes from street view imagery
• Self-Consistency Improves Chain of Thought Reasoning in Language Models
• BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence
• Semantic Understanding of Scenes through the ADE20K Dataset
• Semantic Understanding of Scenes Through the ADE20K Dataset

Related

• UrbanVLP: Multi-Granularity Vision-Language Pretraining for Urban Socioeconomic Indicator Prediction
• StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery
• UrbanFeel: A Comprehensive Benchmark for Temporal and Perceptual Understanding of City Scenes through Human Perspective
• CityRiSE: Reasoning Urban Socio-Economic Status in Vision-Language Models via Reinforcement Learning
• StreetviewLLM: Extracting Geographic Information Using a Chain-of-Thought Multimodal Large Language Model