  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-NN)
retrieval to quickly assess the safety index of the entire city. Experimental
results show that our method outperforms existing training needed deep learning
approaches, achieving efficient and accurate urban safety evaluations. The
proposed automation for urban safety perception assessment is a valuable tool
for city planners, policymakers, and researchers aiming to improve urban
environments.
