Context-Aware Multi-Agent Architecture for Wildfire Insights.

Area of Science:

• Environmental Science
• Artificial Intelligence
• Computer Science

Background:

• Wildfires pose significant global threats, escalating due to climate change and human activities.
• Current wildfire analysis methods often process data in isolation, hindering comprehensive reasoning and transparency.
• Effective wildfire management requires integrated insights for detection, prediction, and risk assessment.

Purpose of the Study:

• To develop a novel orchestrator-based multi-agent system (MAS) for transforming multimodal environmental data into actionable wildfire intelligence.
• To create a transparent and context-aware reasoning framework using Large Multimodal Models (LMMs) and Retrieval-Augmented Generation (RAG).
• To establish a cutting-edge Visual Question Answering (VQA) system for enhanced wildfire decision support.

Main Methods:

• Designed an orchestrator-based multi-agent system (MAS) integrating Large Multimodal Models (LMMs).
• Employed structured prompt engineering and specialized Retrieval-Augmented Generation (RAG) pipelines for advanced reasoning.
• Developed a Visual Question Answering (VQA) system ingesting diverse data: satellite imagery, sensor readings, weather data, and ground footage.

Main Results:

• The system achieved a precision of 0.797 and an F1-score of 0.736 on public wildfire datasets.
• Demonstrated the capability to ingest and process multimodal environmental data for coherent analysis.
• Provided transparent and context-aware reasoning for user queries related to wildfire incidents.

Conclusions:

• The proposed Agentic AI-powered system offers a human-centric solution for wildfire management.
• Empowers firefighters, governments, and researchers with actionable intelligence to mitigate wildfire threats effectively.
• Highlights the potential of multimodal AI in addressing complex environmental challenges like wildfires.