What is Forest Fire Classifier?
Forest Fire Classifier is a tiny web app for detecting fire, smoke, fire+smoke, or no-fire in images. Upload a forest photo and get a prediction in ~500 ms. v2 is rebuilt with Next.js, TypeScript, and TensorFlow.js running server-side for consistent performance. Images are processed in memory only – nothing is stored. It’s open source, privacy-first, and built to showcase how far “college project → real product” can go.
Problem
Users currently rely on manual or less advanced methods (e.g., human observation, basic image analysis) for detecting forest fires and smoke in images, which leads to inconsistent accuracy, slow processing times, and lack of real-time insights.
Solution
A web app powered by TensorFlow.js that enables users to upload forest images and receive instant AI-based predictions (fire, smoke, no-fire) in ~500 ms. It uses server-side processing for consistent performance and ensures privacy by not storing data.
Customers
Environmental agencies, wildfire response teams, forestry researchers, and conservation organizations needing real-time fire detection for proactive disaster management.
Unique Features
Rebuilt with Next.js and TensorFlow.js for server-side AI processing, privacy-first design (no data storage), open-source accessibility, and optimized for rapid college-to-product scalability.
User Comments
Accurate predictions in seconds
Easy to use for non-technical users
Privacy-focused approach is a major plus
Open-source model adds transparency
Ideal for real-time field analysis
Traction
v2 launched with Next.js and TypeScript, showcased as a case study for college projects transitioning to real products. Open-source GitHub repository likely drives community traction, though specific user/MRR data isn't disclosed.
Market Size
The global wildfire detection market was valued at $2.3 billion in 2023 (Global Market Insights, 2023), driven by increasing climate-related disasters and AI adoption in environmental monitoring.
