A New Approach to Assessing Perceived Walkability: Combining Street View Imagery with Multimodal Contrastive Learning Model

Abstract

Walkability is becoming increasingly important in urban planning, public health, and environmental protection. Traditional assessment tools like streetscape images and semantic segmentation focus on objective factors, while questionnaires as the main tool for perceived walkability are limited by cost and scale. This study introduces a new method using the Multimodal Contrastive Learning Model, CLIP, to assess perceived walkability by analysing both tangible and subjective factors such as safety and attractiveness. The method compares perceived with physical walkability by scoring street view images with a customized scale. Initial results indicate CLIP can identify pedestrian-friendly streetscapes that might score low on physical metrics. While its accuracy needs more evaluation, CLIP offers a cost-effective alternative without needing extensive labelled datasets. This method can be combined with objective pedestrian assessment methods to serve as reference information for various industries such as real estate, transportation planning, and tourism.