MSEF fine-tunes VisualGLM-6B with GPT-4-generated soft labels to assess streetscape walkability, safety, and vibrancy, reporting F1 0.84 and 89.3% perception agreement.
Evaluation of Pedestrian-Perceived Comfort on Urban Streets Using Multi-Source Data: A Case Study in Nanjing, China[J]
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Interpretable Multimodal Framework for Human-Centered Street Assessment: Integrating Visual-Language Models for Perceptual Urban Diagnostics
MSEF fine-tunes VisualGLM-6B with GPT-4-generated soft labels to assess streetscape walkability, safety, and vibrancy, reporting F1 0.84 and 89.3% perception agreement.