The Craft: Engineering Features That Respect Time and Space

Once you have sensor readings and a defined prediction target, the next leap in wildfire risk modeling is feature engineering. In this chapter of our journey, we show how we transformed raw numbers into time-aware and space-aware features that improved model performance and operational value. By incorporating rolling statistics, spatial aggregates, and domain-specific interactions, we gave our classifier a sharper and more realistic view of wildfire conditions. The result: predictions that better reflect how risk evolves in the real world.

Introduction: Why Raw Data Isn’t Enough

Our initial model worked — but it was seeing the world one snapshot at a time. In reality, wildfire risk is dynamic, shaped by patterns over minutes, hours, and across landscapes.

As we quickly learned:

“If your model only looks at the now, it’s already too late.”

To improve, we had to teach our model to understand when and where events happen, not just what the sensor says right now.

Temporal Intelligence

Wildfire risk often builds gradually. A sudden temperature spike is more meaningful if it follows hours of dry, windy conditions.

Raw sensor measurements