Real-time disaster response with AI and maps.

A real-time AI-powered platform to report, track, and respond to disasters—saving lives with every click.

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Inspiration

Natural disasters like floods, earthquakes, and fires can cause chaos and panic, especially when communication and response systems are weak. We were inspired by real-world events where people struggled to find safe zones, alert authorities, or coordinate rescue operations. The idea of creating a centralized, real-time disaster coordination platform powered by AI and maps felt like a meaningful way to help communities prepare and respond better.

What it does

Our platform allows users to:

  • Report disasters in real-time (with location and image evidence).
  • View danger zones and nearby safe shelters on an interactive map.
  • Automatically detect disaster types from images using AI (e.g., flood, fire).
  • Prioritize critical incidents based on severity using AI analysis.

It empowers citizens, rescue teams, and volunteers to stay informed and act fast.

How we built it

We built this platform using:

  • Firebase for authentication, database, and hosting.
  • Google Maps API to display real-time incidents and safe zones.
  • Google Vision API to analyze user-uploaded images and detect the disaster type.
  • PaLM (via Google Vertex AI) to generate AI-driven responses and prioritize rescue efforts.
  • React.js for a smooth and dynamic frontend interface.

The system updates in real-time using Firestore, so all users can instantly see the latest data.

Challenges we ran into

  • Setting up the Google Maps API to work seamlessly with Firebase and React.
  • Integrating Google Vision API with image uploads and handling delays in response.
  • Building a lightweight, user-friendly interface while handling complex data behind the scenes.
  • Finding realistic data for testing since we couldn't trigger real disasters!

Accomplishments that we're proud of

  • Created a working end-to-end prototype that combines real-time data, AI, and maps.
  • Successfully integrated multiple Google tools in one responsive system.
  • Built a user-friendly interface that could truly be used during emergencies.

What we learned

  • How to use Google Cloud’s AI services (like Vision and Vertex AI) in practical ways.
  • Working with real-time databases and maps in a high-pressure context.
  • Importance of UI/UX during emergency situations—clarity can save lives.

What's next for Real-time disaster response with AI and maps.

  • Add SMS alert systems using Twilio to reach users without internet.
  • Enable role-based access for citizens, volunteers, and authorities.
  • Train a more advanced AI model for disaster prioritization.
  • Integrate weather APIs for early warnings and prediction.

This project showed us how technology can genuinely save lives when used right. We hope to keep improving and maybe one day deploy it in real communities.

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