AI-Driven Wildfire Mitigation Planning
The planning-side companion to the wildland response demo: a specialized internal intelligence platform that fuses beetle kill, dead-and-down loading, structure density, topography, and historical + live weather trends into ranked mitigation work — thinning, fuel breaks, and defensible space where they protect the most structures per acre — and predicts outbreaks from live lightning. Three planning units along the US 285 corridor through Jefferson and eastern Park counties.
This is a simulated portfolio demo. It is not an official mitigation-planning or emergency-management tool and must not be used for real fuels, evacuation, or land-management decisions.
Roads, subdivisions, burn scars, and hazard framing follow real US 285 corridor geography (Jefferson and eastern Park counties), the 2021 Elk Creek & Inter-Canyon FPD Community Wildfire Protection Plan, and the corridor’s fire history (Hi Meadow 2000, Snaking 2002, Buffalo Creek 1996, Lower North Fork 2012). Every mortality polygon, structure count, density figure, treatment unit, lightning strike, and probability is fictional — no real property, parcel, or survey is depicted.
Where the response demo runs an incident, this one prevents it: the same corridor, viewed through a pre-season fuels baseline, a season risk forecast, and a live overnight lightning watch. Every agent output is simulated from structured mock data behind a clean runPlanAnalysis() seam, so the same UI could later sit on real aerial-survey, lidar, parcel, RAWS, and NLDN lightning feeds — with a human mitigation planner reviewing everything before it becomes work on the ground.
System architecture
Data sources · intelligence platform · planner review · planning outputs
The demo runs on mock data, but the shape is production-minded: slow data (aerial detection surveys, lidar, parcels, CWPPs) and live data (RAWS observations, NLDN lightning) normalize into a shared unit picture; a domain-tuned internal LLM platform — with deterministic fire-behavior models and CWPP retrieval as tools — runs eight planning agents over it; a human planner validates; and the outputs are the artifacts mitigation actually runs on: ranked treatment units, prescriptions, grant packets, and an outbreak watch.
Data sources
The fuels, exposure, and weather record — slow and live.
- USFS aerial detection surveys (beetle kill)
- CSFS forest-health plots
- LANDFIRE fuels & topography
- Lidar canopy + dead-and-down
- County parcel / census density
- CWPPs & fire-history archive
- RAWS history + live obs
- NLDN / GOES GLM live lightning
Intelligence platform
A specialized internal LLM platform, not a general chatbot.
- Domain-tuned model + CWPP retrieval
- Fire-behavior models as tools
- Fuels & Forest Health
- Weather & Climate Trends
- Fire Behavior Modeler
- Structure Exposure
- Lightning & Ignition
- Access & Egress
- Treatment Planner
- Mitigation Copilot
Planner review
A human mitigation planner owns every output.
- Planner validates each finding
- Confidence surfaced per output
- Landowner consent tracked per unit
- CWPP alignment checked
- Full provenance on each claim
Planning outputs
Fundable, sequenced work — and a live watch posture.
- Ranked treatment units (biggest wins)
- Thinning & fuel-break prescriptions
- Parcel outreach lists
- Grant packets (FRWRM / CWDG)
- Outbreak watch + recon tasking
- Pre-position recommendations
What this is, and is not
This is a portfolio demonstration of agentic orchestration applied to the planning side of a domain I know from the other side of the radio. Response gets the drama, but mitigation is where the math is: the demo shows how an internal intelligence platform can turn fuels, exposure, and weather data into a ranked, fundable plan — with confidence surfaced and a human planner in the loop on every output.
The agents are simulated and deterministic. There is no live inference, no real forest-health or parcel data, and no land-management guidance here.
This is a simulated portfolio demo. It is not an official mitigation-planning or emergency-management tool and must not be used for real fuels, evacuation, or land-management decisions.
Roads, subdivisions, burn scars, and hazard framing follow real US 285 corridor geography (Jefferson and eastern Park counties), the 2021 Elk Creek & Inter-Canyon FPD Community Wildfire Protection Plan, and the corridor’s fire history (Hi Meadow 2000, Snaking 2002, Buffalo Creek 1996, Lower North Fork 2012). Every mortality polygon, structure count, density figure, treatment unit, lightning strike, and probability is fictional — no real property, parcel, or survey is depicted.