
The Data Behind the Fire: A Look Into Our Wildfire Model
Wildfires now result in nearly 6 million more hectares of tree cover loss per year than in 2001, an area the size of Croatia.

Modelling Wildfire Risk at Fine Scale
An updated wildfire model for North America and Australia — retrained on two decades of satellite burned-area data and resolved to the scale underwriters price on.
Wildfire · North America × Australia
BirdsEyeView × ESAAn updated wildfire model for North America and Australia — retrained on two decades of satellite burned-area data and resolved to the scale underwriters price on.
Model update · July 2026
01 / Why wildfire is hard to model
Fire is intensely local. Ignition, weather and fuel interact over metres, not regions. Two parcels a short distance apart can carry very different risk on the same day — one continuous, the other green and broken up. Our updated model is focused at a fine scale, for a specific location.
Burned-area training data
2001 – 2025
25 years of fires. Recent years (pink) add a proprietary fuel & moisture layer. Illustrative.
02 / The BirdsEyeView wildfire model
Coverage
North America & Australia
Data for training
Built on 80 input variables, spanning 2001–2025
Fire validation data
ESA Fire CCI + Global Fire Atlas. Two satellite fire data sets.
Additional fuel inputs
Total amount of energy produced by plants, data on plant conditions and structure derived from satellites
Proprietary layer
Fuel & fuel-moisture model, built on ESA data
Resolution
300m resolution
Additional fuel signals
Total amount of energy produced by plants, data on plant health and structure derived from satellites
Proprietary model
Fuel & fuel-moisture, on ESA data
Updated output
Fine-scale wildfire risk
03 / What's changed
Multiple fire targets to learn from
Trained on a harmonised collection of two satellite fire data sets: the ESA Fire Climate Change Initiative and updated Global Fire Atlas data.
Greater coverage
Training data now incorporates fires from 2001 to 2025 — 25 years of observed burned area.
Improved performance
An optimal sampling strategy lifted both precision — how reliably pixels flagged as fire are fire — and recall — how efficiently real fires are detected.
Enhanced explainability
Two complementary approaches explain both model behaviour (what the model relies on) and outcomes (how risk moves with a single variable).
Greater number of input features
Additional measures of fuel — total amount of energy produced by plants and data on plant conditions — feed a proprietary fuel and fuel-moisture model for recent years, built on ESA data. Change in risk now manifests more clearly at fine scales.
04 / Seeing it in CERA®
Wildfire risk is surfaced directly in the CERA® hazard map, resolved fine enough to distinguish exposure within a single region. In-depth wildfire analysis over the Gregory region, Australia — change in risk resolved at fine scale.
Run it against your own book.
The updated model is live. If you write property or wildfire-exposed risk, we will run it against locations in your portfolio and show you the numbers.
Aisling Lynch
aisling.lynch@birdseyeview.aiRory Buckingham
rory.buckingham@birdseyeview.aiSam Clark
sam.clark@birdseyeview.aiSources — Burned-area targets: ESA Fire Disturbance Climate Change Initiative; Global Fire Atlas. Fuel signals: NDVI, gross primary production, and BirdsEyeView's proprietary fuel & fuel-moisture model built on ESA data. Training window 2001–2025. Coverage timeline is illustrative, no absolute scale. BirdsEyeView Technologies Ltd. — Confidential.

The Data Behind the Fire: A Look Into Our Wildfire Model
Wildfires now result in nearly 6 million more hectares of tree cover loss per year than in 2001, an area the size of Croatia.
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