Forest fires have far-reaching consequences, threatening human life, economic stability, and the environment. Understanding the dynamics of forest fires is crucial, especially in high-incidence regions. In this work, we apply deep networks to simulate the spatiotemporal progression of the area burnt in a forest fire. We tackle the region interpolation problem challenge by using a Conditional Variational Autoencoder (CVAE) model and generate in-between representations on the evolution of the burnt area. We also apply a CVAE model to forecast the progression of fire propagation, estimating the burnt area at distinct horizons and propagation stages. We evaluate our approach against other established techniques using real-world data. The results demonstrate that our method is competitive in geometric similarity metrics and exhibits superior temporal consistency for in-between representation generation. In the context of burnt area forecasting, our approach achieves scores of 90% for similarity and 99% for temporal consistency. These findings suggest that CVAE models may be a viable alternative for modeling the spatiotemporal evolution of 2D moving regions of forest fire evolution.

森林火灾具有深远的影响，威胁着人类生命、经济稳定和环境。了解森林火灾的动态至关重要，特别是在高发地区。在这项工作中，我们应用深度网络来模拟森林火灾中燃烧区域的时空进展。我们通过使用条件变分自动编码器（CVAE）模型来解决区域插值问题的挑战，并生成烧焦区域演变的中间表示。我们还应用 CVAE 模型来预测火灾蔓延的进展，估计不同地平线和蔓延阶段的燃烧面积。我们使用真实世界的数据对照其他已建立的技术来评估我们的方法。结果表明，我们的方法在几何相似性度量方面具有竞争力，并且对于中间表示生成表现出卓越的时间一致性。在烧伤面积预测中，我们的方法的相似性得分为 90%，时间一致性得分为 99%。这些发现表明，CVAE 模型可能是对森林火灾演化的二维移动区域的时空演化进行建模的可行替代方案。