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.

翻译：本研究开发了一种轻量级且计算高效的次日野火蔓延预测工具，该工具以多模态卫星数据作为输入。我们提出的深度学习模型——变换域融合UNet（TD-FusionUNet）——引入了可训练的哈达玛变换与离散余弦变换层，这些层执行二维变换，使网络能够在正交化潜在空间中捕获关键的“频率”成分。此外，我们提出了包括随机边缘裁剪和高斯混合模型在内的定制预处理技术，以丰富稀疏火前掩码的表示，并增强模型的泛化能力。TD-FusionUNet在两个数据集上进行了评估：谷歌研究院于2023年发布的次日野火蔓延数据集和WildfireSpreadTS数据集。所提出的TD-FusionUNet以37万参数取得了0.591的F1分数，在参数量显著减少的情况下，超越了WildfireSpreadTS数据集中使用ResNet18作为编码器的UNet基线模型。这些结果表明，所提出的潜在空间融合模型在轻量化设置下实现了准确性与效率的平衡，适用于资源受限环境中的实时野火预测应用。