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

arXiv: https://arxiv.org/abs/2506.02485 · HTML: https://mlsanity.com/paper/2506.02485/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 114 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [MisGAN: Learning from Incomplete Data with Generative Adversarial Networks](https://mlsanity.com/paper/1902.09599/) (2019)
- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [On the Challenges and Opportunities in Generative AI](https://mlsanity.com/paper/2403.00025/) (2024)
- [Promises and challenges of generative artificial intelligence for human learning](https://mlsanity.com/paper/2408.12143/) (2024)
- [WildfireGPT: Tailored Large Language Model for Wildfire Analysis](https://mlsanity.com/paper/2402.07877/) (2024)
- [Training and Serving System of Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2401.02643/) (2024)
- [Generative AI in the Wild: Prospects, Challenges, and Strategies](https://mlsanity.com/paper/2404.04101/) (2024)
- [Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology using Large Language Models -- A Case in Optimizing Intermodal Freight Transportation](https://mlsanity.com/paper/2405.19255/) (2024)
- [A generative model for surrogates of spatial-temporal wildfire nowcasting](https://mlsanity.com/paper/2308.02810/) (2023)
- [A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images](https://mlsanity.com/paper/2502.21151/) (2025)
- [Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement](https://mlsanity.com/paper/2405.19464/) (2024)
- [Automating Bibliometric Analysis with Sentence Transformers and Retrieval-Augmented Generation (RAG): A Pilot Study in Semantic and Contextual Search for Customized Literature Characterization for High-Impact Urban Research](https://mlsanity.com/paper/2410.09090/) (2024)
- [Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management](https://mlsanity.com/paper/2310.01593/) (2023)
- [Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics with Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis](https://mlsanity.com/paper/2412.16098/) (2024)
- [Explainable Global Wildfire Prediction Models using Graph Neural Networks](https://mlsanity.com/paper/2402.07152/) (2024)
- [Empowering Cognitive Digital Twins with Generative Foundation Models: Developing a Low-Carbon Integrated Freight Transportation System](https://mlsanity.com/paper/2410.18089/) (2024)

## Built on by (most cited first, 1 of 1)

- [Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response](https://mlsanity.com/paper/2609.10433/) (2026)

## Abstract (arXiv)

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.