Canadian Startup Develops AI Tool to Predict Forest Fires
Leveraging satellite imagery and machine learning, a new tech solution aims to alert communities faster and help mitigate the devastating impact of wildfires.
Published: June 24, 2025 at 06:33
By: By Lucas Tremblay, Tundra Bulletin
A Calgary-based tech startup is attracting national attention for developing an artificial intelligence system capable of predicting forest fires before they ignite. The system, named EmberEye, uses real-time satellite data and historical fire patterns to assess high-risk zones, giving communities and emergency services crucial time to prepare.
The startup, Borealis Metrics, was founded by a team of environmental scientists and AI engineers. Their platform integrates data from NASA satellites, ground sensors, and weather forecasts to identify regions with dangerously dry conditions and high wind activity. The AI then generates heat maps highlighting areas most susceptible to ignition.
In early tests conducted across Alberta and British Columbia, EmberEye achieved a prediction accuracy rate of over 80%, outperforming existing fire risk models. Local forestry officials say the tool has already helped them redirect resources to potential hotspots before smoke was visible.
“The system doesn’t just flag when fires will happen—it helps us prioritize where to send firebreak crews, helicopters, and communications teams,” said Maya Roslin, a wildfire response coordinator in Kamloops. “That kind of foresight is a game-changer.”
The technology is already drawing interest from federal agencies and conservation groups. Discussions are underway with Parks Canada and Environment and Climate Change Canada about integrating the platform into national wildfire response strategies. International observers, including from California and Australia, are also watching closely.
As climate change intensifies and wildfire seasons lengthen, tools like EmberEye may become essential to Canada’s environmental resilience. Borealis Metrics plans to expand the system’s capabilities to predict landslides and floods—part of a broader vision to make climate-related emergencies more manageable through machine learning.