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New ShearFuse-UNet model predicts wildfire spread efficiently

By PulseAugur Editorial · [2 sources] ·

Researchers have developed ShearFuse-UNet, a novel deep learning model designed for predicting wildfire spread using satellite data. This model is notable for its lightweight architecture and computational efficiency, integrating three distinct transform-domain branches to analyze satellite imagery. It achieves a favorable accuracy-efficiency trade-off, outperforming a larger ResNet18-based U-Net on benchmark datasets. AI

IMPACT This model offers a more efficient approach to wildfire spread prediction, potentially enabling faster and more accessible forecasting for disaster management.

RANK_REASON The cluster describes a new academic paper detailing a novel deep learning model and its performance on specific datasets.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New ShearFuse-UNet model predicts wildfire spread efficiently

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ene Meco, Yingyi Luo, Emadeldeen Hamdan, Adam Watts, Ahmet Enis Cetin ·

    ShearFuse-UNet: Hadamard, DCT, and Shearlet Transform Fusion for Next-Day Wildfire Spread Prediction

    arXiv:2606.14071v1 Announce Type: new Abstract: We propose ShearFuse-UNet, a lightweight and computationally efficient deep learning model for next-day wildfire spread prediction from multi-modal satellite data. The model integrates three complementary transform-domain branches i…

  2. arXiv cs.CV TIER_1 English(EN) · Ahmet Enis Cetin ·

    ShearFuse-UNet: Hadamard, DCT, and Shearlet Transform Fusion for Next-Day Wildfire Spread Prediction

    We propose ShearFuse-UNet, a lightweight and computationally efficient deep learning model for next-day wildfire spread prediction from multi-modal satellite data. The model integrates three complementary transform-domain branches inside each encoder block of a U-Net backbone: a …

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