CanadaFireSat: Towards high-resolution wildfire forecasting with multiple modalities

Canada experienced in 2023 one of the most severe wildfire seasons in recent history, causing damage across ecosystems, destroying communities, and emitting large quantities of CO 2 . This extreme wildfire season is symptomatic of a climate-change-induced increase in length and severity of fire seasons affecting the boreal ecosystem. Therefore, it is critical to empower wildfire management in boreal communities with better monitoring solutions. Wildfire probability maps are an important tool for understanding the likelihood of wildfire occurrence and the potential severity of future wildfires. Fire forecasting tools based on Earth observation data exist, but they are limited both by the lack of label information and by their reliance on coarse-resolution environmental drivers and satellite products, which leads to wildfire occurrence prediction of reduced resolution, typically around ∼ 0 . 1 °. To tackle these two limitations, this paper presents a benchmark dataset, CanadaFireSat available on the Hugging Face Hub , and baseline methods for high-resolution wildfire forecasting. We model wildfire forecasting as a binary patch classification task at 100 m, where each patch is labeled as fire if at least one pixel within the patch has burned at the native label resolution (10 m). CanadaFireSat leverages multi-modal data from high-resolution multi-spectral satellite images (Sentinel-2), mid-resolution satellite products (MODIS), and environmental factors (ERA5). We experiment with convolutional (CNN) and transformer (ViT) architectures. We observe that using multi-modal temporal inputs outperforms single-modal temporal inputs across all metrics, achieving a peak performance of 60.3% in F1 score for the 2023 wildfire season, a season never seen during model training. This demonstrates the potential of multi-modal deep learning for wildfire forecasting at high-resolution and continental scale. The code is available on GitHub for the data generation and the model benchmarking .