# Digital Twin and Agentic AI for Wild Fire Disaster Management: Intelligent Virtual Situation Room

arXiv: https://arxiv.org/abs/2602.08949 · HTML: https://mlsanity.com/paper/2602.08949/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 157 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 22 of 22)

- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [LLM-Assisted Crisis Management: Building Advanced LLM Platforms for Effective Emergency Response and Public Collaboration](https://mlsanity.com/paper/2402.10908/) (2024)
- [Decision support system for Forest fire management using Ontology with Big Data and LLMs](https://mlsanity.com/paper/2405.11346/) (2024)
- [Transfer Learning as an Enabler of the Intelligent Digital Twin](https://mlsanity.com/paper/2012.01913/) (2020)
- [Leveraging Large Language Models for Enhanced Digital Twin Modeling: Trends, Methods, and Challenges](https://mlsanity.com/paper/2503.02167/) (2025)
- [Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data](https://mlsanity.com/paper/2503.14150/) (2025)
- [EdgeChain: An Edge-IoT Framework and Prototype Based on Blockchain and Smart Contracts](https://mlsanity.com/paper/1806.06185/) (2018)
- [An Overview of Cyber Threats, Attacks, and Countermeasures on the Primary Domains of Smart Cities](https://mlsanity.com/paper/2207.04424/) (2022)
- [An Ensemble Machine Learning Approach for Tropical Cyclone Detection Using ERA5 Reanalysis Data](https://mlsanity.com/paper/2306.07291/) (2023)
- [Digital Twinning in Smart Grid Networks: Interplay, Resource Allocation and Use Cases](https://mlsanity.com/paper/2307.06861/) (2023)
- [Facilitating AI and System Operator Synergy: Active Learning-Enhanced Digital Twin Architecture for Day-Ahead Load Forecasting](https://mlsanity.com/paper/2409.00368/) (2024)
- [Synthetic imagery for fuzzy object detection: A comparative study](https://mlsanity.com/paper/2410.01124/) (2024)
- [Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring Data](https://mlsanity.com/paper/2411.01360/) (2024)
- [TwiNet: Connecting Real World Networks to their Digital Twins Through a Live Bidirectional Link](https://mlsanity.com/paper/2411.03503/) (2024)
- [Predictive Digital Twin for Condition Monitoring Using Thermal Imaging](https://mlsanity.com/paper/2411.05887/) (2024)
- [Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices](https://mlsanity.com/paper/2501.15305/) (2025)
- [Digital Twin Based Disaster Management System Proposal: DT-DMS](https://mlsanity.com/paper/2103.17245/) (2021)
- [Estimating fire Duration using regression methods](https://mlsanity.com/paper/2308.08936/) (2023)
- [Smart City Digital Twin Framework for Real-Time Multi-Data Integration and Wide Public Distribution](https://mlsanity.com/paper/2309.13394/) (2023)
- [Evacuation Management Framework towards Smart City-wide Intelligent Emergency Interactive Response System](https://mlsanity.com/paper/2403.07003/) (2024)
- [Contaminant Dispersion Simulation in a Digital Twin Framework for Critical Infrastructure Protection](https://mlsanity.com/paper/2409.01253/) (2024)
- [Data Taxonomy Towards the Applicability of the Digital Twin Conceptual Framework in Disaster Management](https://mlsanity.com/paper/2503.00076/) (2025)

## Abstract (arXiv)

According to the United Nations, wildfire frequency and intensity are projected to increase by approximately 14% by 2030 and 30% by 2050 due to global warming, posing critical threats to life, infrastructure, and ecosystems. Conventional disaster management frameworks rely on static simulations and passive data acquisition, hindering their ability to adapt to arbitrarily evolving wildfire episodes in real-time. To address these limitations, we introduce the Intelligent Virtual Situation Room (IVSR), a bidirectional Digital Twin (DT) platform augmented by autonomous AI agents. The IVSR continuously ingests multisource sensor imagery, weather data, and 3D forest models to create a live virtual replica of the fire environment. A similarity engine powered by AI aligns emerging conditions with a precomputed Disaster Simulation Library, retrieving and calibrating intervention tactics under the watchful eyes of experts. Authorized action-ranging from UAV redeployment to crew reallocation-is cycled back through standardized procedures to the physical layer, completing the loop between response and analysis. We validate IVSR through detailed case-study simulations provided by an industrial partner, demonstrating capabilities in localized incident detection, privacy-preserving playback, collider-based fire-spread projection, and site-specific ML retraining. Our results indicate marked reductions in detection-to-intervention latency and more effective resource coordination versus traditional systems. By uniting real-time bidirectional DTs with agentic AI, IVSR offers a scalable, semi-automated decision-support paradigm for proactive, adaptive wildfire disaster management.