Deepfire AI is an AI-powered wildfire detection and monitoring platform that fuses satellite hotspots, camera detections, lightning strikes, forecast weather, and ground-based sensors to identify potential fires and send intelligent alerts. The system models likely fire spread using terrain, fuels, and wind data to flag communities, roads, and infrastructure at risk, while delivering notifications via SMS, email, WhatsApp, Telegram, and dispatch integrations.
Funding
Funding not disclosed
Founders
Product
Problem
Wildfire detection and response are often delayed because agencies rely on fragmented data sources, manual monitoring, and slow reporting chains. This lag allows small ignitions to grow into large, destructive fires, endangering communities, infrastructure, and natural resources.
Solution
Deepfire AI provides an AI-powered wildfire detection and monitoring platform that combines satellite hotspots, camera detections, lightning strikes, forecast weather, and ground-based sensors into a single intelligence feed. The system automatically identifies new heat anomalies, matches detections with land ownership and protection boundaries, and models likely fire spread using terrain, fuels, and forecast winds. It then sends risk-aware notifications with confidence levels, nearby assets, and wind information through SMS, email, messaging apps, and dispatch integrations. A shared incident workspace gives dispatchers, analysts, and incident commanders a common operating picture, while a map interface displays hotspots, wind, cameras, lightning, historic fire perimeters, and prescribed burns.
Target Audience
Primary customers are wildfire response agencies, emergency management organizations, utility companies, and land management authorities that need early detection and rapid alerting to protect communities and infrastructure.
Features
- Multi-source detection fusion combining satellite hotspots, camera feeds, lightning data, weather forecasts, and ground-based sensors
- Automated land-ownership and protection-boundary matching to identify responsible response agencies
- Fire spread modeling using terrain, fuel loads, and forecast winds to predict risk to communities, roads, and infrastructure
- Risk-aware alerting via SMS, email, WhatsApp, Telegram, REST APIs, and webhooks with confidence scores and asset proximity data
- Shared incident workspace for real-time collaboration among dispatch, analysts, and incident commanders
- GIS, CAD, and RMS integrations for seamless data flow into existing emergency management workflows