AI-Driven Wildfire Hazard Mapping and Sensor Optimization

Use Case:

AI-enhanced pipeline for generating high-resolution wildfire hazard maps and optimizing field sensor deployment to minimize time-to-detection using geospatial and EO data across large territories.

Outcome:

• Fully automated generation of hazard maps across >1M ha of land;
• AI-based sensor placement reduced detection time vs. expert and manufacturer strategies;
• Model accounts for sensor specs, fire spread dynamics, and region-specific hazards;
• Validated performance via empirical comparison with manually produced maps; Scalable modular architecture for rapid deployment and transferability.

Ecosystem Support:

StairwAI support through technical mentoring, commercial roadmap planning, and Stage 2 pilot validation.

AI Relevance:

Demonstrates low-cost AI deployment for climate resilience by:

1. Optimizing civil protection via minimal hardware setups;
2. Enabling municipalities and landowners with automated, expert-level tools;
3. Combining remote sensing and spatial AI models in modular open architectures.

Summary:

OMIKRON Environmental Consultants developed AI-FireMap, an AI-powered solution for wildfire hazard mapping and intelligent sensor placement. The system transforms diverse geospatial and environmental datasets (e.g., topography, vegetation, infrastructure) into accurate, color-coded wildfire hazard maps. Built on an automated pipeline, the AI engine processes vast territories efficiently—tested on over 1 million hectares in Greece—and outputs maps that meet quality benchmarks by comparing them with expert-generated equivalents. For smart sensor placement, OMIKRON designed a heuristic model based on weighted hazard levels. Instead of relying on computationally expensive Monte Carlo simulations, they implemented a weighted-distance algorithm that minimizes the average time-to-detection. This strategy was benchmarked against both manufacturer-defined fixed grid placements and expert-based deployments. As shown in the project’s Pareto efficiency chart (page 6), the AI approach consistently achieved faster detection times using the same number of sensors. Their TRL7 prototype includes a modular architecture, allowing rapid adaptation to different territories. The tool is intended for municipalities, regional civil protection units, private forest owners, and tourism operators. Through strategic outreach (e.g., direct marketing, trade fairs, and online campaigns), the team aims to commercialize the product on a regional scale, offering a transformative, scalable solution for early wildfire detection