Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning
GRID, School of Built Environment, UNSW Sydney, NSW 2052 Australia（GRID，环境建筑学院，新南威尔士大学悉尼分校）；School of Minerals and Energy Resources Engineering, UNSW Sydney, NSW 2052 Australia（矿物与能源资源工程学院，新南威尔士大学悉尼分校）
Haowen Xu, Sisi Zlatanova, Ruiyu Liang, Ismet Canbulat
Wildfires increasingly threaten human life, ecosystems, and infrastructure, with events like the 2025 Palisades and Eaton fires in Los Angeles County underscoring the urgent need for more advanced prediction frameworks. Existing physics-based and deep learning models struggle to capture dynamic wildfire spread across both 2D and 3D domains, especially when incorporating real-time, multimodal geospatial data. This paper explores how generative Artificial Intelligence (AI) models-such as GANs, VAEs, and Transformers-can serve as transformative tools for wildfire prediction and simulation. These models offer superior capabilities in managing uncertainty, integrating multimodal inputs, and generating realistic, scalable wildfire scenarios. We introduce a new paradigm that leverages large language models (LLMs) for literature synthesis, classification, and knowledge extraction, conducting a systematic review of recent studies applying generative AI to fire prediction and monitoring. We highlight how generative approaches uniquely address challenges faced by traditional simulation and deep learning methods. Finally, we outline five key future directions for generative AI in wildfire management, including unified multimodal modeling of 2D and 3D dynamics, agentic AI systems and chatbots for decision intelligence, and real-time scenario generation on mobile devices, along with a discussion of critical challenges. Our findings advocate for a paradigm shift toward multimodal generative frameworks to support proactive, data-informed wildfire response.
Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires
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）
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）
Predictive and Prescriptive AI toward Optimizing Wildfire Suppression
Michael G. Foster School of Business and Paul G. Allen School of Computer Science & Engineering, University of Washington（迈克尔·G·福斯特商学院和保罗·G·阿伦计算机科学与工程学院，华盛顿大学）
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）
Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement
University of Chile（智利大学）；Universidad Tecnológica Metropolitana（大都会技术大学）；Complex Engineering System Institute - ISCI（复杂工程系统研究所）；Rey Juan Carlos University（胡安卡洛斯国王大学）；Centre de Ciència i Tecnologia Forestal de Catalunya（加泰罗尼亚森林科学与技术中心）
