mlsanityData to 14 Oct 2026

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

arXiv 2509.15132 ↗First posted 18 Sep 2025cs.CY cs.CVcited by 0

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 9 of its 37 references that are in the map. First posted on arXiv 18 Sep 2025.

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9 of 37 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence20 Mar
  2. 2024StreetviewLLM: Extracting Geographic Information Using a Chain-of-Thought Multimodal Large Language Model19 Nov
  3. 2023Charting New Territories: Exploring the Geographic and Geospatial Capabilities of Multimodal LLMs24 Nov

Other references · 6

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  4. 2016Semantic Understanding of Scenes through the ADE20K Datasetcited by 1,069
  5. 2023On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligencecited by 88
  6. 2025Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relationscited by 23

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  4. 2026Designing streetscapes from street-view imagery using diffusion models
  5. 2026You Cannot Photograph the Same Street Twice: Reliability Limits in Vision-Language Measurement of Urban Change
  6. 2025CityLens: Evaluating Large Vision-Language Models for Urban Socioeconomic Sensing
  7. 2025From Street Views to Urban Science: Discovering Road Safety Factors with Multimodal Large Language Models
  8. 2025DynamicVL: Benchmarking Multimodal Large Language Models for Dynamic City Understanding