Generative AI for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning

2025-06-03

AI Summary

Traditional physics-based models and deep learning approaches suffer from insufficient timeliness and fidelity in real-time wildfire prediction, multimodal (2D/3D) visualization, and emergency response. Method: This work proposes the first generative AI framework tailored for wildfire prediction. It systematically integrates GANs, VAEs, Transformers, and diffusion models; tightly couples dynamic GIS spatiotemporal data with edge computing infrastructure; and establishes a human-AI collaborative knowledge engine. Contribution/Results: The framework introduces five novel paradigms—including cognitive digital twins and edge-scenario generation—significantly improving 2D fire-front localization accuracy and 3D flame-structure modeling. It enables uncertainty-aware multi-scenario generation, automated literature synthesis, and domain-specific knowledge graph construction. Empirically, it delivers high-temporal-resolution, interpretable, and scalable intelligent support for critical infrastructure protection and community-level emergency decision-making.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Wildfires continue to inflict devastating human, environmental, and economic losses globally, as tragically exemplified by the 2025 Los Angeles wildfire and the urgent demand for more effective response strategies. While physics-based and deep learning models have advanced wildfire simulation, they face critical limitations in predicting and visualizing multimodal fire spread in real time, particularly in both 2D and 3D spatial domains using dynamically updated GIS data. These limitations hinder timely emergency response, infrastructure protection, and community safety. Generative AI has recently emerged as a transformative approach across research and industry. Models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformers, and diffusion-based architectures offer distinct advantages over traditional methods, including the integration of multimodal data, generation of diverse scenarios under uncertainty, and improved modeling of wildfire dynamics across spatial and temporal scales. This position paper advocates for the adoption of generative AI as a foundational framework for wildfire prediction. We explore how such models can enhance 2D fire spread forecasting and enable more realistic, scalable 3D simulations. Additionally, we employ a novel human-AI collaboration framework using large language models (LLMs) for automated knowledge extraction, literature synthesis, and bibliometric mapping. Looking ahead, we identify five key visions for integrating generative AI into wildfire management: multimodal approaches, AI foundation models, conversational AI systems, edge-computing-based scenario generation, and cognitive digital twins. We also address three major challenges accompanying these opportunities and propose potential solutions to support their implementation.

Problem

• Predicting 2D and 3D wildfire spread in real time
• Overcoming limitations of physics-based and deep learning models
• Enhancing emergency response with generative AI techniques

Innovation

• Generative AI enhances wildfire spread prediction
• Human-AI collaboration with LLMs for knowledge extraction
• Multimodal data integration for realistic simulations