Designing Smarter Wildfire Agents: From Reflex to Reasoning on the Edge

Photo by Immo Wegmann on Unsplash

How can artificial agents make fast and rational decisions when deployed in remote forests to detect wildfires? In this article, we outline our plan to simulate and implement twelve types of intelligent agents — ranging from simple reflex to structured utility-based — each designed to interpret real-world wildfire scenarios using satellite data and edge computing. We explain how agent architectures differ by perceptual and decision models and present a roadmap for development over the next few months. Our mission: build smarter GeoAI that doesn’t just react — but reasons.

Introduction: Why Smarter Wildfire Agents Matter

Wildfires are escalating in both frequency and severity. While satellites like MODIS (Moderate-resolution Imaging Spectroradiometer) and VIIRS (Visible/Infrared Imager Radiometer Suite) provide thermal alerts, we face a critical bottleneck: how to turn these data streams into timely, actionable decisions on the ground.

Imagine rugged edge devices installed in remote forests — solar-powered, intermittently connected, and tasked with one job: detect wildfire threats before they escalate. These agents must reason locally:

• Is this heat anomaly a fire?
• Is it in a vegetated area?