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.
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
Clemson University（克莱姆森大学）；NSF National Center for Atmospheric Research（美国国家科学基金会国家大气研究中心）；University of San Francisco（旧金山大学）；Northern Arizona University（北亚利桑那大学）；U.S.D.A. Forest Service, Pacific Wildland Fire Science Laboratory（美国农业部林务局太平洋野火科学实验室）；Georgia Institute of Technology（佐治亚理工学院）
Optimal Wildfire Escape Route Planning for Drones under Dynamic Fire and Smoke
Budapest University of Technology and Economics（布达佩斯技术与经济大学）；HUN-REN Institute for Computer Science and Control (SZTAKI)（HUN-REN计算机科学与控制研究所）；Machine Perception Research Laboratory（机器感知研究实验室）
Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
FireSentry: A Multi-Modal Spatio-temporal Benchmark Dataset for Fine-Grained Wildfire Spread Forecasting
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；College of Soil and Water Conservation, Southwest Forestry University（西南林业大学水土保持学院）；College of Civil Engineering, Southwest Forestry University（西南林业大学土木工程学院）
Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data
Spatial Informatics Group, LLC.（空间信息集团有限责任公司）；ADNET systems, INC.（ADNET系统公司）；NASA Langley Research Center（美国国家航空航天局兰利研究中心）；University at Albany（奥尔巴尼大学）
Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data
Imperial College London（帝国理工学院）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；École des Ponts（巴黎路桥学院）；EDF R&D（法国电力研发中心）；Institut Polytechnique de Paris（巴黎综合理工学院）；University of California, Davis（加利福尼亚大学戴维斯分校）；Hong Kong University of Science and Technology（香港科技大学）；University of Reading（雷丁大学）
Toward Generalizable Deep Learning Based Peatland Fire Detection via Walsh Hadamard Transform and Domain Adaptation
Department of Electrical and Computer Engineering University of Illinois Chicago（电气与计算机工程系伊利诺伊大学芝加哥分校）；Fire and Rescue Department of Malaysia（马来西亚消防与救援部门）；Dept. of Chemical and Environmental Engineering Faculty of Engineering Universiti Putra Malaysia（化学与环境工程系乌拉尔大学马来西亚分校）；International Tropical Forestry Programme Faculty of Tropical Forestry Universiti Malaysia Sabah（国际热带林业计划热带林业系马来西亚沙巴大学）；USDA Forest Service Pacific Wildland Fire Sciences Laboratory（美国农业部森林服务局太平洋野火科学实验室）
ED3R: Energy-Aware Distributed Disaster Detection via Cooperative Agents in Robotic Systems
Dept. of Informatics and Telecommunications, National and Kapodistrian University of Athens（雅典大学信息学与电信系）；Huawei Heisenberg Research Center (Munich)（华为海森堡研究中心（慕尼黑））
