AI and Wildfire Terroir
The French describe the variation in wine flavors as a wine’s “terroir.” Athena uses AI’s pattern recognition to understand the variations in wildfire behavior.
In the age of artificial intelligence, pattern recognition is everywhere. From recommendation engines to climate models, AI systems thrive on structure — and few structures are more deceptively complex than the land beneath our feet.
At Athena Intelligence, we build systems that don’t just index wildfire risk — they profile it. We aim to go beyond surface simplicity and extract layered, data-rich meaning from the physical world. Edward Tufte, the father of modern information design, championed the idea that good information design reveals complexity while preserving clarity. That’s exactly what we aim to do with wildfire.
Indexing vs Profiling: A Fundamental Distinction
Think of an index as a label. It’s fast, flat, and generalized. Traditional wildfire risk models assign an index value to a landscape — “High,” “Moderate,” or “Low.” This is useful for triage, but it conceals variability. Two “High Risk” zones might have entirely different fuels, topography, ignition histories, or wind corridors. An index tells you what the area is. A profile tells you why.
Athena’s approach starts with conditional risk profiles, built from the intrinsic features of the land: slope, aspect, vegetation structure, proximity to wildland-urban interface, and other biophysical variables. These profiles aren’t reactive — they’re foundational. They define how the land would behave if a fire arrived.
We then layer on probabilistic profiles that reflect actual historical fire behavior. Using AI trained on bioregional wildfire histories, these profiles estimate the likelihood that fire will intersect with the land’s conditional potential. The fusion of these two yields Athena’s Risk Class and a more refined, location-specific Risk Score (0–10), which expresses both intensity and probability in a single number.
The Tufte Principle: Rich, Layered, Local
In his landmark work, The Visual Display of Quantitative Information, Tufte emphasized the value of “small multiples” — repeated but nuanced visuals that allow local variation to emerge. Athena’s data works the same way. Our system can process thousands of parcels, each with its own microclimate, fuelscape, and fire probability, and reveal patterns that would be invisible to an index.
Where traditional systems blur land into categories, Athena preserves its nuance. What’s more, our system is hierarchical — risk can be evaluated at parcel, block, neighborhood, or watershed level. Each scale reveals opportunities for different interventions.
From People to Places: Profiles vs Personas
To illustrate why this matters, consider how marketers segment humans. Indexes group people by income, age, or ZIP code. But profiles integrate behaviors, preferences, and histories. That’s why predictive marketing works — it doesn’t just know who you are, it infers what you’ll do and why.
Land behaves in analogous ways. We don’t just want to know where fire has occurred — we want to understand where future fire is motivated by the conditions on the ground, and where that behavior is likely to repeat. This is wildfire terroir — the unique blend of soil, slope, wind, and vegetation that gives fire its local flavor.
John Wanamaker famously said, “Half the money I spend on advertising is wasted; the trouble is I don’t know which half.”
Profiling has changed marketing from the Wanamaker’s experiences in the late 1800's. Today, profiling makes it possible to predict with higher confidence and act with greater precision. Instead of blanketing entire zones with mitigation, communities can invest in the smallest pockets of land that carry the highest compound risk — a combination of likelihood, severity, and value at stake.
AI’s Role in Revealing the Invisible
Artificial intelligence shines in messy, multi-dimensional problems. At Athena, our models don’t attempt to “simplify” wildfire risk into a single satellite snapshot or weather index. Instead, we use AI to reveal structure — to pull patterns from noise, and profiles from terrain.
This is not just about knowing where fire might go. It’s about understanding the pathways, the probabilities, and the profiles that govern wildfire spread. It’s a system that doesn’t just predict fire — it understands its motivation.
In doing so, we give utilities, insurers, governments, and communities tools they can use to make targeted investments that align with the true terroir of fire.
Athena Intelligence: Voice of the Acre® — unlocking the structure of wildfire risk, one profile at a time.
Athena Intelligence is a data vendor with a geospatial, conditional, profiling tool that pulls together vast amounts of disaggregated wildfire and environmental data to generate spatial intelligence, resulting in a digital fingerprint of wildfire risk.
Clients include electric utilities, communities and financial services companies, where Athena’s geospatial intelligence incorporated into multiple products that can be accessed through an online portal. Athena’s data is currently used in Wildfire Mitigation Plans (WMP) and Risk to Financial Impact reporting, Public Safety Power Shutoffs (PSPS), Community Wildfire Protection Plans (CWPP), property insurance underwriting and portfolio risk optimization.
You can learn more by reaching out to me at Elizabeth@AthenaIntel.io and following us on LinkedIn or Energy Central
