Increasing wildfire activity globally has become an urgent issue with enormous ecological and social impacts. In this work, we focus on analyzing and quantifying the influence of landscape topology, understood as the spatial structure and interaction of multiple land-covers in an area, on fire ignition. We propose a deep learning framework, Deep Fire Topology, to estimate and predict wildfire ignition risk. We focus on understanding the impact of these topological attributes and the rationale behind the results to provide interpretable knowledge for territorial planning considering wildfire ignition uncertainty. We demonstrate the high performance and interpretability of the framework in a case study, accurately detecting risky areas by exploiting spatial patterns. This work reveals the strong potential of landscape topology in wildfire occurrence prediction and its implications to develop robust landscape management plans. We discuss potential extensions and applications of the proposed method, available as an open-source software.

在全球范围内增加野火活动已成为一个紧迫的问题，具有巨大的生态和社会影响。在这项工作中，我们专注于分析和量化景观拓扑对着火的影响，景观拓扑被理解为一个区域内多个土地覆盖的空间结构和相互作用。我们提出了一个深度学习框架 Deep Fire Topology，以估计和预测野火点火风险。我们专注于了解这些拓扑属性的影响和结果背后的基本原理，以便为考虑野火点火不确定性的领土规划提供可解释的知识。我们在案例研究中展示了该框架的高性能和可解释性，通过利用空间模式准确检测风险区域。这项工作揭示了景观拓扑在野火发生预测中的强大潜力及其对制定稳健的景观管理计划的影响。我们讨论了所提出方法的潜在扩展和应用，可作为开源软件使用。