发表机构
School for Environment and Sustainability; School of Information; University of Michigan（环境与可持续发展学院; 信息学院; 密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究以底特律为案例，提出基于开源大视觉-语言模型的多视角住宅衰败评估框架，经实验验证该框架可低成本跟踪住房状况，为传统调查提供补充。
AI 中文摘要
过去15年，解决城市衰败问题受到越来越多的关注。评估城市衰败对指导城市规划、针对性开展修复工作以及保障公共卫生至关重要，但传统的住宅衰败调查因劳动成本高、周期长，难以大规模持续开展。本研究提出了一种可扩展的框架，利用开源大视觉-语言模型对多视角数据进行住宅衰败评估。结构化提示引导模型评估房屋属性，包括屋顶完整性、墙体损坏情况以及门窗破损或被封堵的情况，生成二元评估结果和衰败概率估计值。为评估这些视觉评估的性能，我们对比了几位专业人员对这些特征的标注结果，涉及的模型包括基于XGBoost的集成堆叠方法和加权评分系统。结果显示：（i）多视角街景数据有助于提升准确率；（ii）不同大视觉-语言模型的推理优势存在差异；（iii）集成学习器的表现优于单个基础模型，能提升所有住宅状况及衰败评估的鲁棒性。该方法的实际应用可实现低成本的住房存量状况跟踪与管理，为传统衰败调查提供可定期更新的补充方案。
英文摘要
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.
Comments19 pages, 11 figures
