# Can Urban Blight Be Accessed with Vision-language Models: A Case Study in Detroit

arXiv: https://arxiv.org/abs/2608.01753 · HTML: https://mlsanity.com/paper/2608.01753/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agricultural vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 16 of its 60 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Agricultural vision-language models](https://mlsanity.com/mllm/agricultural-vision-language-models/)

## Builds on (most cited first, 16 of 16)

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- [Harnessing Multiple Large Language Models: A Survey on LLM Ensemble](https://mlsanity.com/paper/2502.18036/) (2025)
- [UrbanGPT: Spatio-Temporal Large Language Models](https://mlsanity.com/paper/2403.00813/) (2024)
- [UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web](https://mlsanity.com/paper/2310.18340/) (2023)
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- [CityGPT: Empowering Urban Spatial Cognition of Large Language Models](https://mlsanity.com/paper/2406.13948/) (2024)
- [UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models](https://mlsanity.com/paper/2406.12360/) (2024)
- [OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery](https://mlsanity.com/paper/2504.02866/) (2025)
- [A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text Classification](https://mlsanity.com/paper/2504.18884/) (2025)
- [Generative agents in the streets: Exploring the use of Large Language Models (LLMs) in collecting urban perceptions](https://mlsanity.com/paper/2312.13126/) (2023)
- [Evaluating and Enhancing Trustworthiness of LLMs in Perception Tasks](https://mlsanity.com/paper/2408.01433/) (2024)
- [OpenCity3D: What do Vision-Language Models know about Urban Environments?](https://mlsanity.com/paper/2503.16776/) (2025)
- [Lessons from a human-in-the-loop machine learning approach for identifying vacant, abandoned, and deteriorated properties in Savannah, Georgia](https://mlsanity.com/paper/2407.11138/) (2024)

## Abstract (arXiv)

Addressing urban blight has seen increased focus in the past 15 years. Assessing urban blight is essential for guiding urban planning, targeting rehabilitation, and safeguarding public health, yet traditional residential blight surveys are difficult to maintain at scale due to the labor-intensive cost and long-term cycle. This study introduced a scalable framework for estimating residential blight using open-source large vision-language models on multiple views. Structured prompts guided models to evaluate housing attributes, including roof integrity, wall damage, and broken or boarded openings, producing both binary assessments and probabilistic estimates of disrepair. To evaluate the performance of these visual assessments, we compared professional human annotations of these features across several models, including an ensemble stacking approach based on XGBoost and a weighted scoring system. Results showed that (i) multiple street views can contribute to the improvement of accuracy, (ii) large vision-language models have different strengths of inference, (iii) the ensemble learner outperforms individual base models, enhancing robustness across all residential conditions and blight assessment. The practical application of the method allows low-cost tracking and management of housing stock conditions, providing a regularly updatable complement to traditional blight surveys.