Tea Maps

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Inspiration

During local emergencies such as accidents, harassment, fires, or medical crises, people usually share information through platforms like WhatsApp, Instagram, or Twitter. However, these alerts are often unverified, delayed, and unstructured, which leads to misinformation and confusion. Because of the high noise and fake alert ratio, people gradually lose trust in such messages and often ignore even genuine warnings.

This problem inspired us to build TEA MAPS, a platform that transforms scattered social media alerts into trusted, real-time local signals. By combining location-based reporting, crowd verification, and AI-based validation, the system ensures that emergency alerts are reliable, structured, and visible to the right people at the right time.

What it does

TEA MAPS is a real-time, location-based, crowd-verified alert platform designed to improve community awareness and emergency response.

When an incident occurs:

  1. A user reports the incident with location and supporting evidence.
  2. Only nearby users within a defined radius can verify or reject the alert.
  3. Each user can vote only once, ensuring a fair verification process.
  4. Once a trust threshold is reached, the alert becomes verified.
  5. The incident then appears on a live map showing active and verified alerts.

This system converts social media noise into trusted local information, helping communities respond faster during emergencies.

How we built it

The platform was built using a modern full-stack web architecture.

  • Frontend: React + Next.js for building a responsive and interactive interface
  • Backend: Node.js with Express.js for handling APIs and server logic
  • Database: MongoDB for storing incidents, users, and verification data
  • Real-Time Communication: WebSockets for instant alert updates
  • Authentication: OTP-based login system to ensure secure user identity
  • AI Validation: Image validation model to verify uploaded incident evidence

We also implemented geo-fencing logic so that only users within a specific radius can verify alerts, ensuring that verification is done by people who are physically near the incident.

Challenges we ran into

One of the biggest challenges was preventing fake or misleading reports. Since the system allows users to submit alerts, it was necessary to ensure that the platform did not become another source of misinformation. To address this, we implemented crowd-based verification combined with AI image validation.

Another challenge was designing a fair voting mechanism. We solved this by introducing a one-user-one-vote rule and radius-based voting, which restricts verification only to nearby users.

Maintaining real-time updates and system performance was also challenging. We overcame this by integrating WebSockets to deliver instant notifications and map updates without delays.

What we learned

Through this project, we learned how to build real-time web applications that combine location services, crowd intelligence, and AI validation. We gained hands-on experience with WebSockets, API integration, geo-location systems, and scalable backend architecture.

Most importantly, we learned how technology can be used to create civic-tech infrastructure that improves public safety and community awareness, transforming unreliable social media alerts into a structured and trusted emergency response system.

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