From Points to Patterns: Validating Our Wildfire Agent with Real Data

Photo by Luca Bravo on Unsplash

How do you know an AI agent is thinking spatially the right way? We tested our wildfire decision agent using real-world weather data and registered wildfire reports from Canada. But when our model started seeing fire risks in lakes, we knew we had to dig deeper. This article shares how we validated our rules-based prototype using spatial analysis, what went wrong, and how we designed a better approach using enriched landuse and vegetation context. The result is a smarter GeoAI agent that doesn’t just see the point — it sees the pattern.

The Challenge of Spatial Intelligence in the Wild

In geospatial intelligence, the map is never just the territory. It’s a hypothesis about it.

Our team recently built a prototype wildfire agent: a rule-based system that flags risk zones based on thermal anomalies, humidity, and wind speed. The idea was simple — overlay live weather feeds, use some home-brewed logic, and detect where fires might be spreading.

But validating this prototype meant more than checking code — it meant comparing predictions to actual wildfires on the ground. Canada, with its open wildfire registry, became our testing ground. Every hour, we retrieved fresh incident reports, including ignition coordinates…