WalkCLIP: Multimodal Learning for Urban Walkability Prediction

TL;DR

WalkCLIP is a multimodal framework that combines satellite, street view, and population data to accurately predict urban walkability, addressing limitations of single-source assessments.

Contribution

This paper introduces WalkCLIP, a novel multimodal approach integrating visual and behavioral data for improved urban walkability prediction.

Findings

Outperforms unimodal and multimodal baselines in accuracy

Effective integration of visual and behavioral signals

Reliable predictions across diverse urban locations

Abstract

Urban walkability is a cornerstone of public health, sustainability, and quality of life. Traditional walkability assessments rely on surveys and field audits, which are costly and difficult to scale. Recent studies have used satellite imagery, street view imagery, or population indicators to estimate walkability, but these single-source approaches capture only one dimension of the walking environment. Satellite data describe the built environment from above, but overlook the pedestrian perspective. Street view imagery captures conditions at the ground level, but lacks broader spatial context. Population dynamics reveal patterns of human activity but not the visual form of the environment. We introduce WalkCLIP, a multimodal framework that integrates these complementary viewpoints to predict urban walkability. WalkCLIP learns walkability-aware vision-language representations from GPT-4o generated image captions, refines these representations with a spatial aggregation module that incorporates neighborhood context, and fuses the resulting features with representations from a population dynamics foundation model. Evaluated at 4,660 locations throughout Minneapolis-Saint Paul, WalkCLIP outperforms unimodal and multimodal baselines in both predictive accuracy and spatial alignment. These results show that the integration of visual and behavioral signals yields reliable predictions of the walking environment.