An AI-Driven Framework for Enhancing Urban Walkability using Image Analysis, Augmentation, and Community-Centric Improvements

Keywords: Urban walkability, Generative AI, digital augmentation, pedestrian safety, sidewalk accessibility, perceived safety
Abstract Type: Paper Abstract

Abstract

This project introduces a framework for assessing and enhancing urban walkability using AI-driven image analysis and digital augmentation, applied to the streetscapes of Blacksburg, Virginia. Designed to address more than just proximity to amenities, this framework uses tools like ChatGPT and Google Gemini for evaluating walkability elements, such as sidewalk quality, accessibility, lighting, safety, and pedestrian amenities, and creating visual augmentations that model potential improvements. Beyond static scoring, this adaptable approach offers a dynamic, context-sensitive evaluation that responds to diverse urban forms and considers factors like environmental comfort and perceived safety.

The framework operates in several stages. First, images are systematically captured across the streets of Blacksburg, documenting a variety of streetscapes under consistent conditions. AI then analyzes these images, identifying gaps in walkability that conventional metrics often overlook, such as sidewalk accessibility, environmental comfort, and the influence of design on perceived safety. When low walkability is detected, digital augmentations, including improved sidewalks, added seating, improved lighting, and enhanced pedestrian crossings, are applied to create “before-and-after” visualizations. These visuals illustrate how simple modifications can lead to substantial improvements in pedestrian experience.

To ensure proposed changes reflect local needs and the social context of Blacksburg, this framework incorporates community feedback into the evaluation process, recognizing that walkable spaces should also be desirable and culturally relevant. This framework ultimately provides urban planners with a powerful tool for making data-informed, community-sensitive decisions and offers a replicable model for cities aiming to create healthier, safer, and more equitable pedestrian environments.