Leveraging Multimodal LLMs for Built Environment and Housing Attribute Assessment from Street-View Imagery
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 on SRCC and PLCC relative to the MOS benchmark. To enhance efficiency, we apply knowledge distillation, transferring the capabilities of Gemma 3 27B to a smaller Gemma 3 4B model that achieves comparable performance with a 3x speedup. Further, we distill the knowledge into a CNN-based model (EfficientNetV2-M) and a transformer (...
| Authors | Siyuan Yao, Siavash Ghorbany, Kuangshi Ai et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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