From Sparks to Signals: Designing Our First Wildfire Detection Agent

Photo by Yuheng Ouyang on Unsplash

Can a machine think fast enough to spot fire before it burns out of control? In this article, we introduce our first prototype of a wildfire detection agent. Built as a simple reflex model, this agent evaluates environmental cues like temperature, vegetation, and proximity to assets. The agent doesn’t just detect heat; it speaks the language of risk. Using rule-based logic and spatial reasoning, it transforms raw perceptual data into structured decisions. We discuss why this matters, how we built it, and what this means for the future of geospatial AI in crisis management.

Introduction: A Smarter Way to Watch the Forest Burn

Wildfires are no longer rare events — they are a planetary emergency. Whether fanned by climate extremes or human negligence, the speed and unpredictability of modern fires demand more than traditional response systems. Satellites give us eyes in the sky. Sensors offer streams of raw data. But who — or what — interprets these signals fast enough to make a difference?

Enter the wildfire agent.

Our approach starts with a simple idea: build an autonomous software agent that can perceive real-time environmental data, reason over predefined rules, and decide when to raise an alarm. This first prototype…