EMBR is a wildfire detection network. Camera stations watch the horizon around the clock, cloud AI flags smoke within minutes, and confidence-scored alerts reach the right people. EMBR alerts — a person always decides what happens next.
A working preview. The live model is still in training, so detections aren't wired to cameras yet — everything else is real.
Three stages, each built so nothing quietly gets dropped between them.
Solar-powered poles with pan-tilt-zoom and thermal cameras sweep the horizon and stream frames to the cloud, with on-site storage so nothing is lost if the link drops.
EMBR's own model looks for smoke, then corroborates across cameras and over time — and pins the location by triangulating between stations on real terrain.
Every detection carries a 0–10 confidence score and is routed to the people who chose to hear about that kind of alert — by severity, by area, by their own rules.
EMBR raises a scored alert and gets it to a human. It never sends anyone anywhere on its own — a dispatcher always makes the call.
Each alert is rated out of ten for both "is this really fire?" and "how sure are we of the location," so a shaky signal reads as one.
A detection that isn't confirmed still surfaces; a confirmed fire is kept for good. The system errs toward telling you, not toward silence.
Operators choose which cameras the public can watch, one at a time — off by default. Communities can see the same horizon their responders do.
EMBR is built for the operators responsible for real ground — each with their own cameras, their own people, and their own view.
The public view is live. The operator console is where a network is run.