用视频Transformer+U-Net预测加拿大野火蔓延，三天数据预判次日火情分布。
Spatio-Temporal Wildfire Spread Prediction in Canada using a Video Swin-Hybrid-U-Net and Satellite Imagery
- 融合视频Swin Transformer与卷积解码器，捕捉环境数据时空特征。
- 基于2014-2023年加拿大重大火情数据，准确预测次日火点分布。
- 全公开数据源，可复现性强，适合灾害预警与气候研究者使用。
加拿大野火对生态系统、社区和基础设施构成日益严重的威胁，亟需高精度预报工具支持应对。现有模型常缺乏可扩展性或难以有效捕捉时间动态。本文提出一种深度学习框架，专用于加拿大野火蔓延预测，能有效建模环境数据中的时空模式。采用集成视频Swin Transformer编码器与卷积解码器的U-Net结构，处理连续三天的气象与环境变量序列。数据全部来自Google Earth Engine公开仓库，确保透明性与可扩展性。模型在2014至2023年间加拿大主要野火事件的精选数据集上训练与测试。结果表明，该方法通过时空注意力机制，显著提升次日火点分布预测能力。模型成功捕捉加拿大地貌与时间变化带来的复杂野火动态。该框架为基于公开数据的先进时空野火预报研究及实际应用提供新路径。
原文摘要 · Abstract (English)
Background: Wildfires in Canada present increasing threats to ecosystems, communities, and infrastructure, demanding accurate forecasting tools to aid mitigation efforts. Existing models often lack scalability or fail to capture temporal dynamics effectively. Aims: This study aims to develop a deep learning framework tailored to Canadian wildfire spread prediction that captures spatio-temporal patterns in environmental data. Methods: We propose a U-Net architecture integrating a Video Swin Transformer encoder with a convolutional decoder to model three-day sequences of meteorological and environmental variables. Data are exclusively sourced from public repositories via Google Earth Engine, ensuring transparency and scalability. The model is trained and tested on a curated dataset of major Canadian wildfire events from 2014 to 2023. Key results: Our approach achieves strong predictive performance by effectively leveraging spatio-temporal attention to forecast next-day fire incidence maps. Conclusions: The model successfully captures complex wildfire dynamics unique to Canada's landscape and temporal variability. Implications: This framework paves the way for advanced spatio-temporal wildfire forecasting research and operational applications using publicly accessible datasets.
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