arXiv:2606.24129v1 Announce Type: new Abstract: For a wheelchair user, a standard blue line on a map is often a broken promise. While platforms like OpenStreetMap (OSM) successfully capture where a path is, they frequently fail to convey how it physically feels to travel on it. This information barrier is problematic for wheelchair users. To solve this issue, we present OmniPath, a system that moves from passive mapping to proactive environmental auditing. Our framework fuses the network topology of OSM with the submeter precision of high-density aerial LiDAR (USGS 3DEP) to create a high-fidel
The convergence of advanced AI agentic frameworks, high-resolution geospatial data, and a growing focus on accessibility advocacy makes this development timely.
This system offers a concrete application of AI agents to address real-world accessibility challenges, potentially transforming how urban environments are audited and improved for disabled populations.
The ability to proactively audit environmental accessibility with submeter precision using AI and advanced data sources changes accessibility mapping from a passive documentation to an active, predictive tool.
- · Wheelchair users and accessibility advocates
- · Urban planners and municipal governments
- · Geospatial data providers
- · AI agent developers
- · Traditional manual accessibility auditors
- · Platforms providing only static, low-resolution mapping data
More accurate and actionable data will become available for wheelchair users to plan routes and for cities to prioritize infrastructure improvements.
This methodology could expand beyond wheelchair accessibility to audit other urban design factors, impacting diverse demographic groups and municipal planning processes.
The success of such agentic systems could accelerate the adoption of AI-driven auditing and regulatory compliance across various industries, establishing new standards for infrastructure assessment.
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Read at arXiv cs.AI