Wildfire Risk Intelligence Powered by Multimodal AI
SignalPlusAI helps utilities, insurers, and climate-risk teams forecast short-term wildfire risk using weather, satellite, vegetation, terrain, infrastructure, and historical fire signals.
How It Works
1. Ingest Multimodal Data (Weather, vegetation, satellite, terrain, infrastructure, and historical wildfire data.)
2. Generate Risk Features (Wind exposure, fuel dryness, terrain risk, asset proximity, and historical fire patterns.)
3. Forecast Wildfire Risk (AI models estimate where risk may increase over the next 24–72 hours.)
4. Explain the Drivers (Each risk score includes key contributing factors so teams can understand why an area is elevated.)
The Problem
Wildfire risk is not static.
It changes with wind, temperature, humidity, vegetation dryness, terrain, ignition conditions, asset exposure, and local infrastructure vulnerability. Many teams have access to large amounts of data, but it is still difficult to translate those signals into timely operational decisions.
Utilities, insurers, and emergency-response teams need wildfire intelligence that is:
Local enough to support field and asset-level decisions
Fast enough to reflect changing weather and environmental conditions
Explainable enough to support trust and decision-making
Practical enough to fit into existing workflows
SignalPlusAI is designed to help close that gap.
Our Solution & What We Analyze
SignalPlusAI develops AI models that combine geospatial, environmental, and infrastructure signals to forecast short-term wildfire risk.
Our initial focus is a 72-hour wildfire risk forecast for California, delivered by grid cell, region, or utility segment.
The platform is designed to help teams answer questions such as:
- Which locations may experience elevated wildfire risk in the next 24–72 hours?
- Which utility segments or service areas may require closer monitoring?
- What environmental signals are contributing most to the risk?
- Where should teams prioritize inspection, vegetation review, or operational readiness?
- How can historical fire patterns and current weather conditions be combined into one risk view?
SignalPlusAI brings together multiple categories of data, including:
Weather and Fire-Weather Signals
Wind speed, wind direction, temperature, humidity, precipitation, drought conditions, and fire-weather risk indicators.
Vegetation and Fuel Conditions
Vegetation density, dryness, seasonal fuel patterns, canopy conditions, and vegetation stress indicators.
Satellite and Remote-Sensing Data
Geospatial imagery and remote-sensing signals that help identify land-cover patterns, vegetation changes, terrain conditions, and risk-relevant environmental features.
Terrain and Geography
Slope, elevation, aspect, land-cover type, wildland-urban interface patterns, and geographic exposure.
Infrastructure and Utility Signals
Utility segment locations, service territories, asset exposure, historical outage patterns, and operational constraints where available.
Historical Wildfire and Incident Data
Past wildfire boundaries, ignition patterns, weather conditions during historical events, and regional fire behavior trends.
Pilot Opportunity
SignalPlusAI is seeking early pilot partners in utilities, climate-risk, insurance, emergency planning, and infrastructure resilience.
A pilot can start with one focused geography and one clear output: 72-hour wildfire risk forecast by grid cell, region, or utility segment.
