Deep Fire Topology: Understanding the role of landscape spatial patterns in wildfire susceptibility (Papers Track)
Cristobal Pais (University of California Berkeley); Alejandro Miranda (University of Chile); Jaime Carrasco (University of Chile); Zuo-Jun Shen (University of California, Berkeley)
Increasing wildfire activity across the globe has become an urgent issue with enormous ecological and social impacts. While there is evidence that landscape topology affects fire growth, no study has yet reported its potential influence on fire ignition. This study proposes a deep learning framework focused on understanding the impact of different landscape topologies on the ignition of a wildfire and the rationale behind these results. Our model achieves an accuracy of above 90\% in fire occurrence prediction, detection, and classification of risky areas by only exploiting topological pattern information from 17,579 landscapes. This study reveals the strong potential of landscape topology in wildfire occurrence prediction and its implications for similar research. The proposed methodology can be applied to multiple fields/studies to understand and capture the role and impact of different topological features and their interactions.