About the Project

🔥 FireRiskAI: AI-Powered Wildfire Intelligence

🎯 Inspiration

Wildfires have evolved into a major national security threat, endangering military readiness, infrastructure, and communities. Existing solutions rely on reactive response rather than proactive mitigation. With NOAA restructuring and other agencies facing resource constraints, the need for AI-driven wildfire risk intelligence has never been greater.

Our team, composed of GEOINT experts with experience from NGA, NSA, USGS, tech, and academia, saw an opportunity to apply geospatial AI and predictive modeling to enhance fire risk assessment before ignition.

📚 What We Learned

• The gaps in wildfire intelligence: While many agencies collect wildfire-related data, integrating pre-fire conditions, active constraints, and post-burn severity into a single predictive system remains a challenge.
  
• The role of AI and GEOINT in risk assessment: Machine learning models can process multi-source geospatial data to provide early warnings, mitigation strategies, and decision-support tools.
  
• National security implications: Extreme weather and climate-driven disasters are increasingly classified as national security threats, making wildfire intelligence a critical component of resilience planning.
  

🏗️ How We Built It

FireRiskAI integrates AI-powered geospatial analysis with multiple data sources to assess wildfire risk. Our development process included:

1. Data Integration:
   
   • MTBS (Monitoring Trends in Burn Severity): Helps identify past fire patterns and how hazards turned into disasters.
     
   • NBR (Normalized Burn Ratio): Used for pre- and post-fire vegetation health and burn severity assessments.
     
   • 3D Wind Analysis: Models wind speed, direction, and constraints (terrain barriers, atmospheric conditions) to predict fire spread.
     
   • Climate & Environmental Data: Includes drought levels, temperature anomalies, and fuel availability.
     
2. AI & GIS Implementation:
   
   • Built using ArcGIS, NVIDIA AI, and mimik’s hybrid edge cloud to process and visualize wildfire risk.
     
   • Applied predictive modeling to determine high-risk ignition zones.
     
3. Web-Based Dashboard & API:
   
   • Designed a cloud-based platform to provide interactive geospatial analytics for decision-makers.
     
   • API integration for agencies and industry partners to automate risk assessment workflows.
     

🚧 Challenges We Faced

✅ Data Complexity – Integrating multi-source datasets required aligning different spatial and temporal resolutions to ensure model accuracy.
✅ Predicting Fire Behavior – Fire spread is influenced by multiple dynamic factors, including wind constraints and fuel conditions. Developing a scalable AI model to handle these interactions was a challenge.
✅ Balancing Technical & Operational Needs – FireRiskAI serves both government agencies (NGA, FEMA, DoD) and critical industries (utilities, insurance, infrastructure). Designing a solution that meets operational requirements across sectors required careful consideration.

🌎 The Future of FireRiskAI

We envision FireRiskAI evolving into a real-time intelligence platform that integrates:

• Live satellite and aerial imagery for near-instant fire detection.
  
• Edge AI models that operate in low-connectivity environments for field deployments.
  
• Partnerships with national security agencies to enhance disaster response and climate resilience.
  

🚀 FireRiskAI is more than a wildfire risk tool—it’s a next-generation intelligence system built for national security, infrastructure protection, and disaster resilience.

Built With

• python