基于Video Swin-Hybrid-U-Net和卫星影像的加拿大野火时空蔓延预测

Computer Science > Computer Vision and Pattern Recognition • *arXiv:2606.20693** (cs) [Submitted on 15 Jun 2026] Title:Spatio-Temporal Wildfire Spread Prediction in Canada using a Video Swin-Hybrid-U-Net and Satellite Imagery Authors: Maulik Srivastava (https://arxiv.org/search/cs?searchtype=author&query=Maulik), Esha Saha (https://arxiv.org/search/cs?searchtype=author&query=Saha,+E), Hao Wang (https://arxiv.org/search/cs?searchtype=author&query=Wang,+H) View a PDF of the paper titled Spatio-Temporal Wildfire Spread Prediction in Canada using a Video Swin-Hybrid-U-Net and Satellite Imagery, by Maulik Srivastava and 2 other authors View PDF (https://arxiv.org/pdf/2606.20693) HTML (experimental) (https://arxiv.org/html/2606.20693v1) Abstract: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. Focus to learn more arXiv-issued DOI via DataCite (pending registration) | Submission history From: Maulik Srivastava [view email (https://arxiv.org/show-email/3c304a2d/2606.20693)] • *[v1]** Mon, 15 Jun 2026 05:11:45 UTC (657 KB) Full-text links: Access Paper: View a PDF of the paper titled Spatio-Temporal Wildfire Spread Prediction in Canada using a Video Swin-Hybrid-U-Net and Satellite Imagery, by Maulik Srivastava and 2 other authors • View PDF (https://arxiv.org/pdf/2606.20693) • HTML (experimental) (https://arxiv.org/html/2606.20693v1) • TeX Source (https://arxiv.org/src/2606.20693)  Current browse context: cs.CV < prev (https://arxiv.org/prevnext?id=2606.20693&function=prev&context=cs.CV) \| next > (https://arxiv.org/prevnext?id=2606.20693&function=next&context=cs.CV) new (https://arxiv.org/list/cs.CV/new) \| recent (https://arxiv.org/list/cs.CV/recent) \| 2026-06 (https://arxiv.org/list/cs.CV/2026-06) Change to browse by: cs (https://arxiv.org/abs/2606.20693?context=cs) cs.LG (https://arxiv.org/abs/2606.20693?context=cs.LG) References & Citations • NASA ADS (https://ui.adsabs.harvard.edu/abs/arXiv:2606.20693) • Google Scholar (https://scholar.google.com/scholar_lookup?arxiv_id=2606.20693) • Semantic Scholar (https://api.semanticscholar.org/arXiv:2606.20693) export BibTeX citation Bookmark   Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? 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