Wildfire Detection, Tracking, and Response Coordination Workflow

Manual monitoring of satellite feeds introduces critical delays. A custom workflow with AI agents analyzing thermal and optical imagery in near-real-time can detect ignition signatures within 5-10 minutes of satellite overpass, triggering alerts while fires are still containable. This compression of the OODA loop is the single biggest factor in limiting initial burn area.

Every unchecked acre costs exponentially more in suppression resources, property damage, and economic disruption. Automating the initial analysis and perimeter mapping frees highly skilled GIS and intelligence analysts from watch-keeping, allowing them to focus on complex strategy and modeling. This shifts operations from reactive, labor-intensive monitoring to proactive, intelligence-driven response.

Fragmented communication between agencies (CAL FIRE, USFS, local EOCs) and outdated map-sharing methods slow response. An automated workflow generates standardized, georeferenced perimeter GeoJSON/KML files and alert packages, pushing them directly into agency systems like ESRI ArcGIS Online, RapidSOS, or integrated CAD platforms. This creates a single, trusted source of truth for all responding entities.

Missed detections or delayed notifications carry significant regulatory and reputational risk for monitoring agencies and utilities. An automated, auditable workflow provides a defensible record of detection time, analysis confidence, and notification actions. It systematically applies detection logic across the entire monitored area, eliminating gaps in human attention during shift changes or high-volume periods.

Climate change is increasing the frequency and intensity of fire seasons. A manual process does not scale. A custom workflow built on cloud-native orchestration (e.g., LangGraph for agent coordination, AWS/GCP for processing) can elastically scale to ingest data from multiple constellations (Planet, Sentinel, NOAA) and integrate new data sources like drone feeds or ground sensor networks without a linear increase in operational headcount.

Deployment follows a phased, measurable pilot: First, automate ingestion and detection for a high-risk region, integrating with a single agency endpoint. This 8-12 week pilot delivers a functioning detection engine and quantifies the reduction in time-to-alert. Subsequent phases add tracking models, multi-agency routing logic, and predictive spread modeling, with each stage tied to a specific operational metric like 'acres saved' or 'resource dispatch time.'

AI agents ingest raw thermal and optical data from satellite downlinks (e.g., Landsat, Sentinel, Planet, or commercial constellations) and apply computer vision models to detect pixel clusters exceeding baseline temperatures. The system filters out false positives from industrial heat sources or solar reflection using contextual land-cover data. This automation replaces manual scan-and-interpret tasks, providing the first alert within 5-15 minutes of satellite pass, which is critical for initial attack containment.

Upon detection, a specialized geospatial agent retrieves subsequent passes, wind models, fuel moisture data, and topography to map the active fire perimeter and model 6-12 hour growth vectors using cellular automata or ML-based spread models. The system automatically generates GeoJSON perimeter files and KMZ overlays, updating them as new data arrives. This eliminates the manual GIS work required to create situational awareness maps, directly accelerating ground crew deployment and air tanker routing decisions.

An orchestration layer routes validated alerts and perimeter data to integrated agency systems based on jurisdiction and severity. This involves API integrations with platforms like CAL FIRE's FIRIS, USGS's EDIS, or Esri's ArcGIS Online, and can trigger SMS/email alerts via systems like Everbridge. The workflow includes approval gates for high-confidence alerts and exception routing for ambiguous cases to a human analyst. This ensures the right agency receives machine-readable intelligence without manual data re-entry or phone calls.

A risk assessment agent cross-references the fire perimeter and forecast with live datasets for critical infrastructure (power lines, cell towers), structures (from building footprint databases), and pre-defined evacuation zones. It generates a prioritized risk score for assets and communities, outputting reports for emergency operations centers. This automation provides objective, data-driven prioritization for resource allocation, moving beyond gut-feel decisions during chaotic initial response phases.

The entire workflow is instrumented for observability, logging each detection's confidence score, analyst overrides, and agency response actions. A feedback loop collects ground-truth data from incident reports to retrain detection and spread models. Governance controls ensure all automated alerts are traceable, and a human-in-the-loop review mode is maintained for low-confidence events or politically sensitive regions. This closed-loop system is essential for maintaining operational trust and continuously improving accuracy.

A production build uses a LangGraph or Temporal-based orchestrator to manage the multi-agent workflow. It ingests data from ground station APIs or cloud buckets (AWS S3, GCP Cloud Storage), processes imagery using PyTorch or TensorFlow models on GPU-enabled cloud instances, and stores outputs in a spatiotemporal database (PostGIS). Critical integration points include satellite operator tasking APIs for follow-up imagery, geospatial alerting platforms, and emergency agency data portals. Deployment is typically phased, starting with detection and expanding to full orchestration.