Long-horizon wildfire risk forecasting requires generating probabilistic spatial fields under sparse event supervision while maintaining computational efficiency across multiple prediction horizons. Extending diffusion models to multi-step forecasting typically repeats the denoising process independently for each horizon, leading to redundant computation. We introduce N-Tree Diffusion (NT-Diffusion), a hierarchical diffusion model designed for long-horizon wildfire risk forecasting. Fire occurrences are represented as continuous Fire Risk Maps (FRMs), which provide a smoothed spatial risk field suitable for probabilistic modeling. Instead of running separate diffusion trajectories for each predicted timestamp, NT-Diffusion shares early denoising stages and branches at later levels, allowing horizon-specific refinement while reducing redundant sampling. We evaluate the proposed framework on a newly collected real-world wildfire dataset constructed for long-horizon probabilistic prediction. Results indicate that NT-Diffusion achieves consistent accuracy improvements and reduced inference cost compared to baseline forecasting approaches.

翻译：长时域野火风险预测需要在稀疏事件监督下生成概率空间场，同时保持跨多个预测时域的计算效率。将扩散模型扩展至多步预测通常需要为每个时域独立重复去噪过程，导致冗余计算。本文提出N-Tree扩散模型（NT-Diffusion），一种专为长时域野火风险预测设计的层次化扩散模型。火灾事件被表示为连续的火险地图（FRMs），其提供的平滑空间风险场适用于概率建模。NT-Diffusion无需为每个预测时间戳运行独立的扩散轨迹，而是共享早期去噪阶段并在后续层级进行分支，在减少冗余采样的同时实现时域特异性优化。我们在新收集的真实世界野火数据集上评估所提框架，该数据集专为长时域概率预测构建。结果表明，相较于基线预测方法，NT-Diffusion在实现精度持续提升的同时降低了推理成本。