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  3. Context-aware Multi-agent Architecture For Wildfire Insights.
  1. Home
  3. Context-aware Multi-agent Architecture For Wildfire Insights.

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Context-Aware Multi-Agent Architecture for Wildfire Insights.

Ashen Sandeep1, Sithum Jayarathna1, Sunera Sandaruwan1

  • 1Department of Computer Science and Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka.

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|February 13, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an AI system that analyzes diverse wildfire data for better insights. It enhances decision-making for effective wildfire management and prevention.

Keywords:
artificial intelligencedisaster managementmultimodal RAGsustainabilityvisual question answering

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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.