A Multi-Agent System for Context-Aware Wildfire Management

Introduction

FusionSense is a domain-specific Visual Question Answering (VQA) system developed to support context-aware wildfire management through an orchestrator-based multi-agent architecture. The system leverages Large Multimodal Models (LMMs) augmented with specialized Retrieval-Augmented Generation (RAG) pipelines to jointly reason over heterogeneous environmental data. In particular, the proposed multimodal RAG framework integrates dedicated Image Data Retrieval and CSV Data Retrieval pipelines, coordinated by a central Orchestrator Agent. The image retrieval pipeline processes satellite imagery, UAV-based thermal and RGB data, and aerial photographs to detect fire signatures, smoke patterns, and burn severity indicators. In parallel, the CSV retrieval pipeline handles structured meteorological measurements, historical wildfire records, and sensor-derived environmental attributes to provide contextual grounding. Retrieved visual and tabular evidence is fused through a multimodal RAG mechanism and passed to a Reasoning Agent, enabling explainable, human-in-the-loop decision support via a conversational user interface. This design moves beyond black-box prediction by delivering transparent, evidence-grounded responses tailored to dynamic wildfire scenarios.

Climate Change & Wildfires

In recent decades, climate change and global warming have been major concerns among experts. Due to these phenomena, there has been an increase in global temperature, severe droughts with less rainfall, and hazardous winds caused by hurricanes. These environmental changes have contributed significantly to the frequency and severity of wildfires worldwide. The loss of forest also contributes to an increase in Carbon Dioxide, which itself is a greenhouse gas, contributing to global warming, which ultimately causes more wildfires, causing a chain reaction.

Annual Number of Wildfires Worldwide

Annual Area Burned by Wildfires

Our Mission

To develop an AI-powered Visual Question Answering system that enables timely understanding of wildfires by reasoning over diverse environmental data, ultimately supporting better decision-making for wildfire management and environmental protection.

PROJECT OVERVIEW

The Challenge

Wildfires are increasing in frequency and intensity due to climate change, with devastating ecological, social, and economic impacts. Current monitoring systems struggle with:

Processing multimodal data effectively
Providing real-time, contextual insights
Making complex data accessible to decision-makers
Integrating diverse information sources

Our Solution

An AI-powered Visual Question Answering framework that:

Processes Multiple Data Types: Satellite imagery, weather readings, sensor data, and audio signals
Multi-Agent Collaboration: Specialized AI agents work together for comprehensive analysis
Natural Language Interface: Users can ask questions in plain English and get intelligent responses
Real-time Insights: Provides timely, context-aware information for decision support

Why This Matters

64,897 wildfires reported in 2024 vs 56,580 in 2023 (USA)
8.9 million acres consumed in 2024 vs 2.7 million in 2023
Early detection can save lives, property, and ecosystems
AI can process vast amounts of data faster than human analysts

TECHNOLOGY & INNOVATION

System High level diagram

Core Technologies

1. Orchestrator-Based Multi-Agent Architecture

Centralized Orchestration: Utilizes an intelligent Orchestrator Agent to dynamically decompose complex user queries into parallelizable sub-tasks rather than linear execution.
Specialized Agentic Workflow: Coordinates distinct "Data Acquisition" and "Reasoning" agents to handle specific aspects of wildfire management.
Dynamic Tool Routing: Implements an adaptive routing logic that selects optimal tools based on query intent and data needs, ensuring scalable and flexible task execution.

2. Multimodal Retrieval-Augmented Generation (RAG)

Cross-Modal Data Integration: Features a specialized RAG framework that synthesizes heterogeneous data streams—including tabular meteorological data and visual imagery—into a unified reasoning context.
Specialized Retrieval Pipelines: Operates three distinct pipelines:
- CSV RAG: Retrieves structured meteorological and historical fire data.
- Image RAG: Retrieves visual analogs from satellite or aerial imagery databases.
- Multimodal RAG: Synchronizes text and pixel data for joint reasoning.
Raw Artifact Retrieval: Retrieves raw image artifacts in Base64 format to prevent information loss associated with image-to-text captioning.

3. Explainable Visual Question Answering (VQA)

Natural Language Interface: Enables authorities to query environmental data using natural language and images (e.g., "How will the fire spread based on this image and wind speed?").
Human-in-the-Loop Decision Support: Moves beyond "black-box" predictions to provide transparent, text-based reasoning that explains why a specific alert or strategy is recommended.
Strategic Real-Time Response: Delivers verified, hallucination-free intelligence suitable for high-stakes decision-making.

4. Context-Aware LMM Reasoning

Large Multimodal Models (LMMs): Leverages state-of-the-art models (e.g., GPT-5-Nano) to analyze semantic intent and reason jointly over pixel inputs and text.
Structured Prompt Engineering: Utilizes a formal context engineering framework (Role, Goal, Task Vector, Constraints) to ensure deterministic task execution and minimize hallucinations.
Grounding & Validation: Validates outputs against retrieved evidence to ensure high precision (0.797) and reduce false alarms in critical scenarios.

Technical Architecture

Our system features a modular, scalable architecture:

Orchestrator: Manages query processing and task distribution
Specialized Agents: Handle specific data types and analysis tasks
RAG Pipelines: Retrieve and integrate relevant information
Conversational Interface: User-friendly natural language interaction

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