Screenshot from NYU's Digital Fire Twin prototype showing a near-real-time wildfire spread forecast generated approximately three hours before the observed fire perimeter for Grasshopper Fire, Oregon. The blue outline shows the model-predicted fire perimeter, the red outline shows the actual observed perimeter, and the overlap metrics indicate strong agreement between the forecast and observed fire spread. Credit: NYU Tandon School of Engineering

A new AI system that NYU Fire Research Group describes as a digital twin of an active wildfire has, in early testing, shown the ability to forecast how a fire will spread in near real time.

It continuously updates its forecast as new data arrives from satellites, weather feeds, terrain, fuel, wildland–urban interface, structures, critical infrastructure, vegetation maps, drones, ground sensors and crews in the field.

The lead research team, comprising research associate professor Prabodh Panindre, research scientist Anandu Nair and global network professor Sunil…