Finalist · FIRST Global Challenge 2026 · Top 5 of 81 countries

A forest can't scream
for help. So we gave it a voice.

FireSonics is a network of low-cost microphones that listens to the forest and flags a fire within minutes — using sound, which travels around the smoke and terrain that block cameras and satellites.

LIVE INPUT — NODE 04 listening
ambient forest audio 44.1kHz
01

Fire moves faster than we can see it.

Hundreds of forest fires ignite every year before anyone notices. Every method we rely on today has the same blind spot: it needs a clear line of sight.

Human observation
Needs someone to see or smell it first — and their sense of distance and direction is often rough, which delays and misdirects the response.
Cameras
Depend on a direct sightline. Dense vegetation, terrain, darkness, and the fire's own smoke all work against it.
Satellites
Cover enormous areas, but weren't built to catch a fire in the first minutes after ignition.

Sound travels around the obstacles that block sight. A fire's crackle carries through trees and even through smoke — which is why FireSonics listens, instead of watching.

02

From a crackle in the woods to an alert on a map.

Every node runs the same short pipeline, continuously.

1

Listen

A microphone at the node picks up the ambient forest sound.

2

Transmit

The audio is sent from the node to a central server.

3

Analyze

An AI model checks the sound against the acoustic pattern of fire.

4

Decide

The model classifies the sound as fire, or not fire.

5

Alert

The node's known location is used to estimate where the fire is.

03

Sound is a hard signal, on purpose.

Burning material makes a distinct crackle and pop — but a forest is loud in its own right, and a system that cries wolf loses its usefulness fast. The real engineering problem isn't recognizing a fire recording. It's telling a real fire apart from a forest doing a good impression of one, without missing the real thing.

  • Wind moving through branches
  • Rain on leaves and ground
  • Insects and animals
  • Vehicles and machinery
  • People and forestry work
  • Distant, unrelated human activity
04

Teaching a model what fire sounds like.

~7,000
recordings in the training dataset
9 m
fire audio detected through a wall, in a blind test
live
inference runs directly on streamed microphone audio

The model is trained to recognize patterns in audio, rather than a fixed threshold — so it can pick up on the more complex, less obvious features of a fire's sound. To test whether it had actually learned those features, we played it fire recordings it had never heard during training. It correctly picked out a fire sound played from a phone in another room, roughly 9 meters away.

Next priority: false positives. We're expanding the dataset with wind, rain, and human activity, and testing how much the choice of microphone changes what the model can hear.
05

The node, kept intentionally simple.

A forest isn't watched by one sensor — it takes many. So each node has to be cheap enough to deploy at scale, without giving up on reliability.

Estimated cost / node~€80
Core sensorMicrophone + on-board electronics
PlacementDistributed across forest terrain
Trade-offs balancedMic quality, power, range, cost

A lower price per unit is what makes a real network possible — but it can't come at the cost of missing the fire it was built to hear.

06

One sensor can't watch a forest. A network can.

07

FIRST Global Challenge 2026 — Phase 2.

This year's theme, 'Igniting Innovation', is about forest fires.

5 / 81
finalist teams out of countries entered
Phase 2
refining the model and hardware for Incheon

In Incheon, we plan to demonstrate the full pipeline, live: nodes placed around a simulated forest, exposed to a mix of real forest sounds, human activity, and a fire. A convincing demo has to do two things at once — catch the fire, and ignore everything else.

08

Built by GPHTech.

FireSonics is the New Technologies Experience project of GPHTech, the robotics team at Gymnázium Pavla Horova in Michalovce, Slovakia — 1st place at FIRST Global Slovakia, and one of 5 finalist teams worldwide.

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