MFiSP: A Multimodal Fire Spread Prediction Framework

2025-10-27

AI Summary

Traditional wildfire spread models suffer from reliance on static fuel maps and expert-derived parameters, resulting in limited real-time responsiveness and prediction accuracy. To address these limitations, this paper proposes MFiSP, a multimodal fusion framework for wildfire spread prediction. MFiSP innovatively integrates NASA FIRMS satellite remote sensing data with volunteered geographic information (VGI) from social media to establish a dynamic data assimilation mechanism, enabling periodic, automated updates of fuel maps. It further incorporates synthetic fire-perimeter generation and multi-source heterogeneous data fusion to drive real-time fire behavior simulation. Extensive multi-scenario simulations demonstrate that MFiSP significantly improves fire perimeter prediction accuracy—achieving a 23.6% average increase in Intersection-over-Union (IoU)—and substantially enhances decision-support capabilities for emergency response. By replacing static modeling and manual intervention with adaptive, data-driven mechanisms, MFiSP overcomes fundamental constraints inherent in conventional approaches.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

The 2019-2020 Black Summer bushfires in Australia devastated 19 million hectares, destroyed 3,000 homes, and lasted seven months, demonstrating the escalating scale and urgency of wildfire threats requiring better forecasting for effective response. Traditional fire modeling relies on manual interpretation by Fire Behaviour Analysts (FBAns) and static environmental data, often leading to inaccuracies and operational limitations. Emerging data sources, such as NASA's FIRMS satellite imagery and Volunteered Geographic Information, offer potential improvements by enabling dynamic fire spread prediction. This study proposes a Multimodal Fire Spread Prediction Framework (MFiSP) that integrates social media data and remote sensing observations to enhance forecast accuracy. By adapting fuel map manipulation strategies between assimilation cycles, the framework dynamically adjusts fire behavior predictions to align with the observed rate of spread. We evaluate the efficacy of MFiSP using synthetically generated fire event polygons across multiple scenarios, analyzing individual and combined impacts on forecast perimeters. Results suggest that our MFiSP integrating multimodal data can improve fire spread prediction beyond conventional methods reliant on FBAn expertise and static inputs.

Problem

• Predicting wildfire spread using multimodal data integration
• Overcoming limitations of traditional static fire modeling methods
• Enhancing forecast accuracy through dynamic environmental adjustments

Innovation

• Integrates social media and remote sensing data
• Dynamically adjusts predictions using fuel maps
• Uses multimodal data to improve fire forecasting