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Forest Fire Classifier
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Forest Fire Classifier

Real-time wildfire detection powered by advanced machine learning. From research to production.

Model v2 โ€ข 98.5% accuracy
Active
99.9%
Uptime
< 500ms
Response
10k+
Requests

Project

  • AboutProject story & tech stack
  • ResearchTechnical details & methodology
  • API DocsDeveloper documentation

Resources

  • Live Wildfire MapReal-time wildfire tracking
  • DatasetForest Fire C4 on Kaggle
  • Research PaperAcademic publication

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ยฉ 2025 Built by ๐Ÿ—๏ธ osLabs

- with Next.js, TensorFlow.js, and lots of โ˜•.

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From Dorm Room to Deployment

What started as a 6th-semester college project has evolved into a professional AI showcase. Saving forests, one pixel at a time.

The Origin Story

It was 2020. The world was in lockdown, the deadlines were tight, and I needed a 6th-semester project that wasn't just another 'To-Do List' app.

I wanted to build something with real-world impact. Forest fires were devastating ecosystems globally, and I wondered: could a simple web cam and some AI help catch them early? Armed with a dataset and a lot of determination, I dove into the world of Computer Vision.

Fast forward to today: The code is cleaner, the UI is snappier (thanks, Next.js!), and the model is smarter. But the core mission remains the same: leveraging technology to protect our planet.

The Arsenal

The modern tools and technologies powering this project

TensorFlow.js

TensorFlow.js

AI / ML

The brain behind the operation. Powers our server-side inference engine.

Next.js 16

Next.js 16

Framework

The muscle. Server-side rendering, routing, and pure speed.

TypeScript

TypeScript

Language

The safety net. Catching bugs before they catch fire.

Tailwind CSS

Tailwind CSS

Styling

The stylist. Making sure we look good while saving the world.

Framer Motion

Framer Motion

Animation

The magic. Smooth animations that make you go "ooh".

Vercel

Vercel

Infrastructure

The launchpad. Deploying to the edge with a single git push.

By The Numbers

Key metrics that showcase the project's quality (and my caffeine addiction)

Model Accuracy

98.5%

Model Accuracy

Validated on Forest Fire C4 set

Caffeine Intake

โˆž

Caffeine Intake

Cups of coffee

Images Trained

4,823

Images Trained

Forest Fire C4 training corpus

Requests Served

10k+

Requests Served

Since v2 beta launch

The Timeline

The complete development journey from concept to deployment

The Spark (Sem 6)

2020

The Spark (Sem 6)

2020

The initial idea. Scouring Kaggle for datasets and training the first clunky Python model.

  • Idea conceived
  • Dataset created
  • First prototype

The Paper

2023

The Paper

2023

Refining the methodology. "Learning without Forgetting" became the core research focus. Published in Fire Ecology.

  • Research published
  • Methodology refined
  • Peer reviewed

The Rewrite (v2)

2025

The Rewrite (v2)

2025

Ditching the old HTML/JS for a modern Next.js stack. Better UI, faster inference, and actual type safety.

  • Next.js migration
  • UI overhaul
  • Performance boost

The Future

Beyond

The Future

Beyond

Adding real-time satellite data, edge deployment support, and maybe a dark mode that is actually dark.

  • Global domination
  • Saving more trees

Blast from the Past

Want to see where it all started? Check out the original v1 prototype.

๐Ÿ•ฐ๏ธ

Stalk Me (Professionally)

Like what you see? Want to hire me? Or just want to argue about which JS framework is best?

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