U-Net with Hadamard Transform and DCT Latent Spaces for Next-day Wildfire Spread Prediction
This provides a computationally efficient tool for real-time wildfire prediction in resource-limited environments, though it appears incremental as it builds on existing UNet architectures with novel transform layers.
The researchers tackled next-day wildfire spread prediction by developing a lightweight deep learning model called TD-FusionUNet that incorporates trainable Hadamard Transform and DCT layers, achieving an F1 score of 0.591 with 370k parameters while outperforming a UNet baseline.
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.