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Modelling Wildfire Risk at Fine Scale

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.

25 Years Burned Area Training Data
80 Input Variables
300m Resolution

Wildfire · North America × Australia

BirdsEyeView × ESA

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.

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

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Proprietary model

Fuel & fuel-moisture, on ESA data

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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.

European Space Agency backed

Sources — 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.

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