We use machine learning (ML) to predict the relative loss and survivability probabilities for individual structures from a wildland fire event.
Comprised of a set of factors, our models can be used for the tactical placement of protective assets during a fire event, as an evaluation method for regional mitigation efforts, or as an assessment of structural risk.
As of today, we have data available for over 3 million structures across the state of California.