Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data

Abstract Facing the escalating threat of global wildfires, numerous computer vision techniques using remote sensing data have been applied in this area. However, the selection of deep learning methods for wildfire prediction remains uncertain due to the lack of comparative analysis in a quantitative and explainable manner, crucial for improving prevention measures and refining models. This study aims to thoroughly compare the performance, efficiency, and explainability of four prevalent deep learning architectures: Autoencoder, ResNet, UNet, and Transformer‐based Swin‐UNet. Employing a real‐world data set that includes nearly a decade of remote sensing data from California, U.S., these models predict the spread of wildfires for the following day. Through detailed quantitative comparison analysis, we discovered that Transformer‐based Swin‐UNet and UNet generally outperform Autoencoder and ResNet, particularly due to the advanced attention mechanisms in Transformer‐based Swin‐UNet and the efficient use of skip connections in both UNet and Transformer‐based Swin‐UNet, which contribute to superior predictive accuracy and model interpretability. Then we applied XAI techniques on all four models, this not only enhances the clarity and trustworthiness of models but also promotes focused improvements in wildfire prediction capabilities. The XAI analysis reveals that UNet and Transformer‐based Swin‐UNet are able to focus on critical features such as “Previous Fire Mask”, “Drought”, and “Vegetation” more effectively than the other two models, while also maintaining balanced attention to the remaining features, leading to their superior performance. The insights from our thorough comparative analysis offer substantial implications for future model design and also provide guidance for model selection in different scenarios. The source code for this project is publicly available as open source on Zenodo (Y. Zhou et al., 2024, https://doi.org/10.5281/zenodo.14286931 ).