Towards Human-AI Accessibility Mapping in India: VLM-Guided Annotations and POI-Centric Analysis in Chandigarh

TL;DR

This paper adapts the Project Sidewalk platform for Chandigarh, India, using VLM-guided annotations to map sidewalk accessibility and identify infrastructure improvements across diverse city sectors.

Contribution

It introduces modifications for deploying the platform in India, including VLM-based guidance and POI-centric analysis, expanding its applicability to new urban contexts.

Findings

AI-guidance scored 4.66 on utility

Identified 1,644 infrastructure improvement locations

Mapped accessibility across 40 km of sidewalks in Chandigarh

Abstract

Project Sidewalk is a web-based platform that enables crowdsourcing accessibility of sidewalks at city-scale by virtually walking through city streets using Google Street View. The tool has been used in 40 cities across the world, including the US, Mexico, Chile, and Europe. In this paper, we describe adaptation efforts to enable deployment in Chandigarh, India, including modifying annotation types, provided examples, and integrating VLM-based mission guidance, which adapts instructions based on a street scene and metadata analysis. Our evaluation with 3 annotators indicates the utility of AI-mission guidance with an average score of 4.66. Using this adapted Project Sidewalk tool, we conduct a Points of Interest (POI)-centric accessibility analysis for three sectors in Chandigarh with very different land uses, residential, commercial and institutional covering about 40 km of sidewalks. Across 40 km of roads audited in three sectors and around 230 POIs, we identified 1,644 of 2,913 locations where infrastructure improvements could enhance accessibility.