用哈达玛与DCT变换提升野火预测效率，轻量化模型更适配资源受限场景。
U-Net with Hadamard Transform and DCT Latent Spaces for Next-day Wildfire Spread Prediction
- 在正交潜空间中融合哈达玛与DCT变换，捕捉关键频率特征。
- 参数仅37万，F1达0.591，显著优于同类轻量模型。
- 适合实时野火预测，尤其适用于算力有限的野外部署。
我们开发了一种轻量且计算高效的工具，用于基于多模态卫星数据进行次日野火蔓延预测。所提出的深度学习模型名为变换域融合U-Net（TD-FusionUNet），引入可训练的二维哈达玛变换和离散余弦变换层，使网络能在正交化潜空间中捕捉关键“频率”成分。此外，我们设计了定制预处理方法，包括随机边缘裁剪和高斯混合模型，以增强稀疏火前掩码的表征能力，提升模型泛化性能。在两个数据集上评估：2023年谷歌研究发布的Next-Day Wildfire Spread数据集与WildfireSpreadTS数据集。结果表明，该模型在参数仅37万的情况下，取得0.591的F1分数，超越使用ResNet18作为编码器的基线U-Net模型，且参数量大幅减少。这证明所提潜空间融合模型在轻量化设置下实现了准确率与效率的良好平衡，适用于资源受限环境中的实时野火预测应用。
原文摘要 · Abstract (English)
We developed a lightweight and computationally efficient tool for next-day wildfire spread prediction using multimodal satellite data as input. The deep learning model, which we call Transform Domain Fusion UNet (TD-FusionUNet), incorporates trainable Hadamard Transform and Discrete Cosine Transform layers that apply two-dimensional transforms, enabling the network to capture essential "frequency" components in orthogonalized latent spaces. Additionally, we introduce custom preprocessing techniques, including random margin cropping and a Gaussian mixture model, to enrich the representation of the sparse pre-fire masks and enhance the model's generalization capability. The TD-FusionUNet is evaluated on two datasets which are the Next-Day Wildfire Spread dataset released by Google Research in 2023, and WildfireSpreadTS dataset. Our proposed TD-FusionUNet achieves an F1 score of 0.591 with 370k parameters, outperforming the UNet baseline using ResNet18 as the encoder reported in the WildfireSpreadTS dataset while using substantially fewer parameters. These results show that the proposed latent space fusion model balances accuracy and efficiency under a lightweight setting, making it suitable for real time wildfire prediction applications in resource limited environments.
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