Fighting Fire with Data
Wildfires are increasing in frequency and intensity due to climate change. Every minute of delayed detection can mean thousands of additional acres burned and lives endangered.
Our satellite-based early warning system detects fires within minutes of ignition, enabling rapid response that saves lives, property, and irreplaceable natural resources.
Detection Methodology
Thermal Imaging Acquisition
Continuous monitoring using VIIRS, MODIS, and commercial thermal sensors to detect heat anomalies across monitored regions with sub-hourly revisit times.
Multi-Spectral Analysis
Combine thermal bands with visible and near-infrared imagery to distinguish active fires from volcanic activity, industrial heat sources, and sun glint.
AI Fire Classification
Deep learning models trained on historical fire events classify detected anomalies, estimate fire intensity (FRP), and predict spread direction.
Weather Integration
Real-time integration with meteorological data including wind speed, humidity, and temperature to model fire behavior and spread potential.
Alert Generation
Automated alerts with fire location, estimated size, spread prediction, and recommended evacuation zones delivered to emergency responders.
Key Features
Early Detection
Detect fires within minutes of ignition using thermal anomaly detection algorithms optimized for speed and accuracy.
Spread Prediction
Physics-based fire behavior models predict spread direction and speed based on terrain, fuel type, and weather conditions.
Risk Assessment
Pre-fire risk mapping identifies areas of high ignition potential based on vegetation dryness, weather forecasts, and historical patterns.
Damage Assessment
Post-fire analysis quantifies burned area, severity levels, and impacts on infrastructure and natural resources.
Scientific Foundation
Our fire detection algorithms are built on established research and validated against thousands of documented fire events.
Deep Learning for Wildfire Detection from Satellite Imagery
Ban, Y., Zhang, P., Nascetti, A., et al.
VIIRS Active Fire Detection Algorithm: Theoretical Background
Schroeder, W., Oliva, P., Giglio, L., et al.
Machine Learning for Wildfire Risk Prediction
Jain, P., Coogan, S.C.P., Subramanian, S.G., et al.
Satellite-Based Fire Monitoring: A Review of Algorithms and Systems
Giglio, L., Schroeder, W., Justice, C.O.
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Like Never Before?
Join leading organizations using our platform to monitor, predict, and respond to environmental changes in real-time.