IGNIS processes satellite thermal data, weather models, and terrain analysis to detect fires early, predict spread, and coordinate multi-agency response — because wildfire seasons are getting longer and the detection-to-response window is the single most important variable in outcomes.
| System | Specification | Value |
|---|---|---|
| Thermal Detection | VIIRS 375m + MODIS 1km, dual-satellite | Sub-15 min |
| Weather Integration | NOAA GFS + HRRR + local stations | 3km grid |
| Terrain Analysis | USGS 10m DEM + LANDFIRE fuel models | Full US |
| Fuel Moisture | Live + dead fuel moisture from NFDRS | Daily update |
| Spread Modeling | Rothermel-based + ML correction | 85% accuracy |
| Evacuation | Road network + population + dynamic zones | Real-time |
| Smoke Forecasting | HYSPLIT dispersion model integration | 48h forecast |
| Communication | CAP alerts + agency radio + SMS | Multi-channel |
| Damage Assessment | Sentinel-2 post-fire burn severity | 10m resolution |
| Seasonal Risk | Drought index + vegetation + climate forecast | Monthly |
| Phase | Window | Coverage | Agencies | Detection Improvement |
|---|---|---|---|---|
| Single Agency | Months 1-6 | 1 fire district | 1 | Proof of concept |
| Regional Network | Months 7-12 | State-level | 5-10 | 40% faster detection |
| Interstate | Year 1-2 | Multi-state corridors | 50+ | Cross-jurisdiction coord |
| National | Year 2-3 | Continental US+CA+AU | 200+ | Sub-10-min detection |
| Global Network | Year 3+ | All wildfire regions | 1,000+ | Unified global fire intel |
Estimated cost reduction → $0.02 per alert
Faster detection means smaller fires, fewer resources deployed, less damage to structures, infrastructure, and ecosystems — catching fires while they can still be stopped.
Wildfire seasons are getting longer, more severe, and more expensive. In the US alone, the 2025 fire season burned over 7 million acres. Globally, fire-affected area has expanded significantly in the past decade, driven by drought, heat, and land use changes. The detection-to-response window is the single most important variable in wildfire outcomes, and it is consistently extended by coordination delays between agencies that were never built to share data in real time.
The data to detect and predict wildfires exists. NASA's FIRMS provides satellite thermal detection globally. NOAA publishes weather forecasts and fire weather indices. USGS maintains topographic and vegetation data critical for spread modeling. But these data sources sit in separate systems, updated on different schedules, and the fire agencies responsible for response are often using radio, spreadsheets, and phone calls to coordinate across jurisdictions.
IGNIS connects the detection, prediction, and response layers into a single coordination system. It processes satellite thermal data for early fire detection, runs spread prediction models that integrate weather, terrain, and fuel load data, and coordinates resource allocation across agencies. The firefighters stay on the line. IGNIS handles the logistics behind them.
Early Detection
Processes NASA FIRMS satellite thermal imagery and ground sensor data to identify fire ignition points within minutes, not hours.
Spread Prediction
Runs fire behavior models integrating real-time weather data, topographic profiles, vegetation moisture content, and historical burn patterns.
Response Coordination
Maps available resources across agencies and jurisdictions, generates deployment recommendations, and tracks resource allocation in real time.
Evacuation Planning
Generates evacuation zone recommendations based on fire spread predictions, road network capacity, and population density analysis.
Resource Intelligence
Tracks crew availability, equipment deployment, air support status, and supply chain logistics across all participating agencies.
Recovery Monitoring
Monitors post-fire areas via satellite for reforestation needs, erosion risk assessment, and long-term ecosystem recovery tracking.