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We present a novel framework for automatically evaluating building conditions nationwide in the United States by leveraging large language models (LLMs) and Google Street View (GSV) imagery. By fine-tuning Gemma 3 27B on a modest human-labeled dataset, our approach achieves strong alignment with human mean opinion scores (MOS), outperforming even individual raters relative to the MOS benchmark in terms of SRCC and PLCC. To enhance efficiency, we apply knowledge distillation, transferring the capability of Gemma 3 27B to a smaller Gemma 3 4B model, which attains comparable performance with a 3 \(\times \) speedup. Further, we distill the knowledge into a CNN-based model (EfficientNetV2-M) and a transformer (SwinV2-B), delivering close performance while achieving a 30 \(\times \) speed gain. Our framework offers a flexible and efficient solution for large-scale building condition assessment, enabling high accuracy with minimal human labeling effort.

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Leveraging Multimodal LLMs for Building Condition Assessment from Street-View Imagery

  • 会议论文

  • 发表日期：

    2026年

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  • Siyuan Yao,
  • Siavash Ghorbany,
  • Meghan Forstchen,
  • Alexis Korotasz,
  • Matthew Sisk,
  • Ming Hu,
  • Chaoli Wang

摘要

We present a novel framework for automatically evaluating building conditions nationwide in the United States by leveraging large language models (LLMs) and Google Street View (GSV) imagery. By fine-tuning Gemma 3 27B on a modest human-labeled dataset, our approach achieves strong alignment with human mean opinion scores (MOS), outperforming even individual raters relative to the MOS benchmark in terms of SRCC and PLCC. To enhance efficiency, we apply knowledge distillation, transferring the capability of Gemma 3 27B to a smaller Gemma 3 4B model, which attains comparable performance with a 3 \(\times \) speedup. Further, we distill the knowledge into a CNN-based model (EfficientNetV2-M) and a transformer (SwinV2-B), delivering close performance while achieving a 30 \(\times \) speed gain. Our framework offers a flexible and efficient solution for large-scale building condition assessment, enabling high accuracy with minimal human labeling effort.