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

• URL: http://arxiv.org/abs/2602.08949v1
• Date: Mon, 09 Feb 2026 17:44:52 GMT
• Title: Digital Twin and Agentic AI for Wild Fire Disaster Management: Intelligent Virtual Situation Room
• Abstract summary: wildfire frequency and intensity are projected to increase by approximately 14% by 2030 and 30% by 2050 due to global warming.<n> Conventional disaster management frameworks rely on static simulations and passive data acquisition.<n>We introduce the Intelligent Virtual Situation Room (IVSR), a bidirectional Digital Twin (DT) platform augmented by autonomous AI agents.
• Score: 1.4323566945483497
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• D3ARC: Time-Critical Distributed Disaster Detection for Asynchronous Cooperative Multi-Robot Systems [3.3540424603831323]
  Climate change is increasing the severity and unpredictability of natural disasters.<n>Traditional monitoring practices remain limited by coverage, cost, and personnel risk.<n>This paper introduces D3ARC, an asynchronous distributed hierarchical framework for time-aware and reliable wildfire detection.
  arXiv  Detail & Related papers  (2026-09-07T11:14:24Z)
• Synthetic-to-Real Pipeline for Safe Landing Zone Detection [0.0]
  This work proposes a comprehensive perception and data generation pipeline designed to bridge the sim-to-real gap for autonomous landing tasks.<n>We introduce a procedural synthetic data engine that generates urban environments with automated semantic annotations through domain randomization.<n>A deterministic landing module utilizes a Euclidean Distance Transform (EDT) and dynamic inference logic to identify the largest inscribed safe landing zones.
  arXiv  Detail & Related papers  (2026-06-09T01:32:07Z)
• Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning [56.240199425429445]
  Multi-robot motion planning requires each robot to generate collision-free trajectories from local observations.<n>Most existing planners generate trajectories from a static snapshot of the local observation.<n>This paper introduces Simulation-Informed Diffusion (SID), a decentralized framework built on constraint-aware diffusion models.
  arXiv  Detail & Related papers  (2026-05-26T21:17:53Z)
• RADAR: Closed-Loop Robotic Data Generation via Semantic Planning and Autonomous Causal Environment Reset [48.645870795753105]
  We introduce Robust Autonomous Data Acquisition for Robotics (RADAR)<n>RADAR is a fully autonomous, closed-loop data generation engine that completely removes human intervention from the collection cycle.<n>In simulation, our framework achieves up to 90% success rates on complex, long-horizon tasks.
  arXiv  Detail & Related papers  (2026-03-12T11:18:52Z)
• Agentic AI Meets Edge Computing in Autonomous UAV Swarms [3.9444299467643025]
  Agentic AI, powered by large language models (LLMs), with autonomous reasoning, planning, and execution, opens new operational possibilities.<n>However, infrastructure constraints, dynamic environments, and the computational demands of multi-agent coordination limit real-world deployment.<n>This paper investigates the integration of LLM-based agentic AI and edge computing to realize scalable and resilient autonomy in UAV swarms.
  arXiv  Detail & Related papers  (2026-01-20T19:45:33Z)
• Optimization-Guided Diffusion for Interactive Scene Generation [52.23368750264419]
  We present OMEGA, an optimization-guided, training-free framework that enforces structural consistency and interaction awareness during diffusion-based sampling.<n>We show that OMEGA improves generation realism, consistency, and controllability, increasing the ratio of physically and behaviorally valid scenes.<n>Our approach can also generate $5times$ more near-collision frames with a time-to-collision under three seconds.
  arXiv  Detail & Related papers  (2025-12-08T15:56:18Z)
• Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL [37.79695337425523]
  Low-altitude intelligent networks (LAINs) can support mission-critical applications such as disaster response, environmental monitoring, and real-time sensing.<n>These systems face key challenges, including energy-constrained UAVs, task arrivals, and heterogeneous computing resources.<n>We propose an integrated air-ground collaborative network and formulate a time-dependent integer nonlinear programming problem that jointly optimize UAV trajectory planning and task offloading decisions.
  arXiv  Detail & Related papers  (2025-12-05T08:14:45Z)
• Trajectory Design for UAV-Based Low-Altitude Wireless Networks in Unknown Environments: A Digital Twin-Assisted TD3 Approach [62.11847362756054]
  Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN)<n>We propose a digital twin (DT)-assisted training and deployment framework.<n>In this framework, the UAV transmits integrated sensing and communication signals to provide communication services to ground users, while simultaneously collecting echoes that are uploaded to the DT server to progressively construct virtual environments (VEs)<n>These VEs accelerate model training and are continuously updated with real-time UAV sensing data during deployment, supporting decision-making and enhancing flight safety.
  arXiv  Detail & Related papers  (2025-10-28T10:05:53Z)
• Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning [4.582541339132966]
  Wildfires increasingly threaten human life, ecosystems, and infrastructure.<n>Existing physics-based and deep learning models struggle to capture dynamic wildfire spread across both 2D and 3D domains.<n>This paper explores how generative Artificial Intelligence (AI) models can serve as transformative tools for wildfire prediction and simulation.
  arXiv  Detail & Related papers  (2025-06-03T05:54:40Z)
• Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving [55.96227460521096]
  Vision-Language Models (VLMs) have been integrated into autonomous driving systems to enhance reasoning capabilities.<n>We propose a natural reflection-based backdoor attack targeting VLM systems in autonomous driving scenarios.<n>Our findings uncover a new class of attacks that exploit the stringent real-time requirements of autonomous driving.
  arXiv  Detail & Related papers  (2025-05-09T20:28:17Z)
• Agent-Based Modeling and Deep Neural Networks for Establishing Digital Twins of Secure Facilities under Sensing Restrictions [0.0]
  Digital twin technologies help practitioners simulate, monitor, and predict undesirable outcomes in-silico.<n>Virtual reality (VR) based digital twin technologies are especially useful when monitoring human Patterns of Life (POL) in secure nuclear facilities.<n>High-security status of such facilities may restrict modelers from deploying human activity sensors for data collection.
  arXiv  Detail & Related papers  (2025-03-29T17:01:43Z)
• Generative Diffusion-based Contract Design for Efficient AI Twins Migration in Vehicular Embodied AI Networks [55.15079732226397]
  Embodied AI is a rapidly advancing field that bridges the gap between cyberspace and physical space. In VEANET, embodied AI twins act as in-vehicle AI assistants to perform diverse tasks supporting autonomous driving.
  arXiv  Detail & Related papers  (2024-10-02T02:20:42Z)
• Simulation-based Scenario Generation for Robust Hybrid AI for Autonomy [0.12499537119440242]
  HAMERITT is a simulation-based autonomy software framework. It supports the training, testing and assurance of neuro-symbolic algorithms for autonomous maneuver and perception reasoning.
  arXiv  Detail & Related papers  (2024-09-10T16:00:26Z)
• Wildfire Autonomous Response and Prediction Using Cellular Automata (WARP-CA) [0.7252027234425334]
  WARP-CA model integrates terrain generation using Perlin noise with the dynamism of Cellular Automata (CA) to simulate wildfire spread. We explore the potential of Multi-Agent Reinforcement Learning to manage wildfires by simulating autonomous agents, such as UAVs and UGVs.
  arXiv  Detail & Related papers  (2024-07-02T19:01:59Z)
• HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments [93.94020724735199]
  HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
  arXiv  Detail & Related papers  (2024-01-23T18:59:43Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.