Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly a decade of remote-sensing data and historical fire records to predict wildfires. This prediction problem is framed as three machine learning tasks. Results are compared and analyzed for four different deep learning models to estimate wildfire likelihood. The results demonstrate that deep learning models can successfully identify areas of high fire likelihood using aggregated data about vegetation, weather, and topography with an AUC of 83%.

翻译：确定极有可能发生野火的地区是土地和林业管理及备灾的一个关键组成部分。我们通过汇总近十年遥感数据和历史火灾记录来创建一套数据,以预测野火。这一预测问题被设计为三种机器学习任务。对四个不同的深层学习模型的结果进行了比较和分析,以估计野火的可能性。结果显示深层学习模型能够利用植被、天气和地形综合数据,成功地确定高火灾可能性地区,澳大利亚联合自卫军占83%。