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Revolutionizing Urban Safety Perception Assessments: Integrating Multimodal Large Language Models with Street View Images by Zhang, Jiaxin; Li, Yunqin; Fukuda, Tomohiro; Wang, Bowen is a scholarly article available to read on EtoBox.

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Measuring urban safety perception is an important and complex task that traditionally relies heavily on human resources. This process often involves extensive field surveys, manual data collection, and subjective assessments, which can be time-consuming, costly, and sometimes inconsistent. Street View Images (SVIs), along with deep learning methods, provide a way to realize large-scale urban safety detection. However, achieving this goal often requires extensive human annotation to train safety ranking models, and the architectural differences between cities hinder the transferability of these models. Thus, a fully automated method for conducting safety evaluations is essential. Recent advances in multimodal large language models (MLLMs) have demonstrated powerful reasoning and analytical capabilities. Cutting-edge models, e.g., GPT-4 have shown surprising performance in many tasks. We employed these models for urban safety ranking on a human-annotated anchor set and validated that the results from MLLMs align closely with human perceptions. Additionally, we proposed a method based on the pre-trained Contrastive Language-Image Pre-training (CLIP) feature and K-Nearest Neighbors (K-

Author

Zhang, Jiaxin; Li, Yunqin; Fukuda, Tomohiro; Wang, Bowen

Published

2024

Language

EN