AccessPath Go: Making Outdoor Walks More Accessible with Local Gemma

This is a submission for the Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass.

What I Built

AccessPath Go — Go farther. Look at your phone less. ♿🌿

What if planning an outdoor walk meant thinking about more than just distance?

For people with mobility challenges, the shortest walking route is not always the most suitable one. Stairs, steep slopes, uneven surfaces, and unexpected obstacles can make an ordinary walk difficult.

I built AccessPath Go, an AI-powered accessibility companion that helps people make more informed decisions about outdoor walking routes.

The idea behind it is simple: technology should help people explore the outdoors with more confidence, not keep them staring at their phones.

Key Features

• Accessibility preferences: Specify preferences such as avoiding stairs, steep slopes, and unpaved paths.
• Walking directions: Generate walking routes with distance and estimated travel time using OpenRouteService.
• Interactive map: Explore route information through a Leaflet-based map.
• AI-powered barrier analysis: Upload a photo and use a locally running Gemma model to identify possible walking barriers.
• Human-in-the-loop reporting: Review AI-generated findings before choosing whether to share a community report.
• Community reports: Help other users become aware of reported barriers.
• Touch Grass mode: Keep the experience focused on the outdoor journey.

The goal is not to claim that every route is accessible, but to help users consider potential barriers before setting out.

Demo

Here is a walkthrough of AccessPath Go, including its route-planning interface, accessibility preferences, AI photo analysis, and community reporting workflow.

Watch the full demo on YouTube

Code

GitHub repository: Stuti-Singhania/accesspath-go

The repository includes the application code, setup instructions, architecture details, and development guidance.

How I Built It

I wanted to combine full-stack development with AI that addresses a practical, real-world problem.

The project uses:

• Frontend: Next.js, React, TypeScript, Tailwind CSS, and Leaflet.
• Backend: Python, FastAPI, and Pydantic.
• AI: Gemma 3 running locally through Ollama.
• Routing: OpenRouteService.
• Persistence: SQLite.

The frontend handles accessibility preferences, route selection, map interactions, photo uploads, and report review. The FastAPI backend coordinates application requests, route generation, image analysis, and data persistence.

One of the most interesting parts was integrating local Gemma inference into the barrier-reporting workflow. The model analyzes an uploaded image and returns a potential barrier classification, severity, and description for the user to review.

For example, a photo might show stairs, a steep hillside path, or an ambiguous stone wall. The application presents the result for human review instead of automatically publishing every model prediction.

This distinction matters: an image model can describe what it sees, but it cannot always determine whether an object actually blocks a walking route.

Why Does Open Innovation Matter?

Accessibility is not a problem that one developer or one company can solve for everyone.

Open innovation makes it possible for developers, accessibility advocates, and community members to share ideas, identify weaknesses, and contribute improvements. Projects like AccessPath Go can become more useful when people with different mobility needs help shape their design.

Open-source technologies also made it possible for me to experiment with a complete AI-powered workflow. By using Gemma locally through Ollama, I could build the photo-analysis feature without depending on a hosted model API for that inference.

Local inference offers more control over the image-analysis step, but the entire application is not fully offline: routing requests and map resources may still require external services.

I also believe accessibility software should be developed with feedback from the people who may use it. This project is a prototype and a starting point for that process, not a substitute for lived experience or verified accessibility information.

My Agent Session

I used coding assistance during development to inspect the codebase, refine implementation details, and review changes. I also ran tests and checked the application behavior rather than assuming that generated code or AI predictions were automatically correct.

My approach is to use AI tools to speed up development while keeping testing, review, and human judgment part of the process.

Prize Categories

Best Use of Gemma

AccessPath Go uses Gemma 3 locally through Ollama to analyze photos of potential walking barriers. The model supports an accessibility-focused workflow in which users can review and confirm findings before sharing reports.