Rampage!

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Inspiration

Rampage was inspired by a simple but important idea: disabled individuals deserve equal access to social spaces. We wanted to create an app where users could easily find accessible establishments like cafes, parks, or restaurants. Often, finding information about accessibility is difficult, so our solution was to create a Waze-like app for accessibility, crowdsourcing data from users about the features of these spaces, including ramps, elevators, and accessible restrooms. Users can also filter places based on the accessibility features they require.

What it does

Rampage allows users to upload images and details about the accessibility features of various social spaces. The app processes these images, detects key features, and stores them in a database, helping other users discover inclusive venues.

How we built it

We built the frontend using Swift, SwiftUI, and MapKit for location-based browsing and ease of use. The backend was developed using Flask and Firebase Firestore for real-time data storage and Google Cloud Vision API for image analysis, identifying features like ramps and elevators from user-submitted photos. We also explored integrating Perplexity AI for validating detected features when Google Vision’s accuracy fell short.

Challenges we ran into

One major challenge was the limitations of Google Cloud Vision, which struggled to detect essential accessibility features like wheelchair ramps. We also ran into time constraints as first-time hackers, which prevented us from fully integrating the frontend and backend, although both function independently.

Accomplishments that we're proud of

We are proud of developing a fully functional backend and a responsive front-end interface, despite the time limitations. The merging of user-contributed data and geolocation-based tracking was a huge milestone for us.

What we learned

We learned the importance of flexibility when working with APIs and how to adapt quickly when encountering limitations. We also learned a lot about time management and estimating the scope of a project. We feel like creating a whole app that integrates with AI and has social features was a very ambitious idea for a 24 hour hackathon, especially our very first one. Most importantly, we explored technologies we had not previously worked with and gained a lot of technical learning experience.

What's next for Rampage!

We plan to refine the accessibility feature detection improving the AI integration and focus on a complete frontend-backend integration. We believe Rampage has the potential to help disabled individuals access social spaces more easily and will continue to develop it into a seamless tool for crowdsourcing accessibility. All in all, we hope to continue working on Rampage after the Hackathon, and possibly use it as my Immersion Vanderbilt project next year!

Built With

• computer-vision
• firebase
• flask
• google-cloud
• mapkit
• perplexity
• python
• rest-api
• swift
• swiftui