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Paying attention to wildfire: using U-Net with attention blocks on multimodal data for next day prediction

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dc.contributor.authorFitzgerald, Jack, author
dc.contributor.authorSeefried, Ethan, author
dc.contributor.authorYost, James, author
dc.contributor.authorPallickara, Sangmi, author
dc.contributor.authorBlanchard, Nathaniel, author
dc.contributor.authorACM, publisher
dc.date.accessioned2024-11-11T19:34:33Z
dc.date.available2024-11-11T19:34:33Z
dc.date.issued2023-10-09
dc.description.abstractPredicting where wildfires will spread provides invaluable information to firefighters and scientists, which can save lives and homes. However, doing so requires a large amount of multimodal data e.g., accurate weather predictions, real-time satellite data, and environmental descriptors. In this work, we utilize 12 distinct features from multiple modalities in order to predict where wildfires will spread over the next 24 hours. We created a custom U-Net architecture designed to train as efficiently as possible, while still maximizing accuracy, to facilitate quickly deploying the model when a wildfire is detected. Our custom architecture demonstrates state-of-the-art performance and trains an order of magnitude more quickly than prior work, while using fewer computational resources. We further evaluated our architecture with an ablation study to identify which features were key for prediction and which provided negligible impact on performance.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationJack Fitzgerald, Ethan Seefried, James Yost, Sangmi Pallickara, and Nathaniel Blanchard. 2023. Paying Attention to Wildfire: Using U-Net with Attention Blocks on Multimodal Data for Next Day Prediction. In International Conference on Multimodal Interaction (ICMI '23), October 09-13, 2023, Paris, France. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3577190.3614116
dc.identifier.doihttps://doi.org/10.1145/3577190.3614116
dc.identifier.urihttps://hdl.handle.net/10217/239531
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofPublications
dc.relation.ispartofACM DL Digital Library
dc.rights© Jack Fitzgerald, et al. | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ICMI '23, https://dx.doi.org/10.1145/3577190.3614116.
dc.subjectwildfire prediction
dc.subjectmultimodal
dc.subjectdeep learning architectures
dc.subjectattention
dc.titlePaying attention to wildfire: using U-Net with attention blocks on multimodal data for next day prediction
dc.typeText

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