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From Sensors to Signals: How We Began Building a Wildfire Risk Classifier
How can raw environmental sensor data be transformed into actionable wildfire risk intelligence? This post shares the first chapter of our journey — how we designed a clean data foundation, clarified what we wanted to predict, and overcame the challenge of starting without labeled fire event data. By combining careful database design with weak-label bootstrapping, we created a functional baseline for wildfire risk detection. These early steps laid the groundwork for more sophisticated geospatial intelligence that blends machine learning with operational rules.
Why This Story Matters?
Wildfires are not just natural events — they are fast-moving threats to ecosystems, communities, and critical infrastructure. The difference between containing a fire and watching it spread often comes down to minutes.
When we set out to create a wildfire reflex agent, our goal was simple in words but complex in execution:
Detect early signs of wildfire risk before ignition, and act fast enough to make a difference.
This required bridging three worlds: geospatial data acquisition, predictive analytics, and operational decision-making. Our starting point? A stream of environmental readings from distributed locations simulating sensors. Our challenge? Turning those readings into reliable, real-time intelligence.
