In Figures 2 and 3, we provide examples of simulated and real satellite image sequences of wildfire. We implement the S2R-FireTr model for wildfire forecasting and backtracking using PyTorch. We set the batch size to 4. Each training sequence contains six frames; each resized to 256 We train S2R-FireTr for ten epochs. In Table 1, we present the performance of S2R-FireTr, which is trained with different temporal intervals. Satellite orbits around the Earth typically involve relatively large temporal intervals, and this aligns with the training data.

We prepare 1K real-world wildfire scenarios for testing the AI models. We also propose a deep transformer, S2R-FireTr, which excels in considering the multi-modal environmental information for forecasting and backtracking the wildfire.

In Figures 2 and 3, we provide examples of simulated and real satellite image sequences of wildfire. We implement the S2R-FireTr model for wildfire forecasting and backtracking using PyTorch. We set the batch size to 4. Each training sequence contains six frames; each resized to 256 We train S2R-FireTr for ten epochs. In Table 1, we present the performance of S2R-FireTr, which is trained with different temporal intervals. Satellite orbits around the Earth typically involve relatively large temporal intervals, and this aligns with the training data.