论文标题
从历史遥感数据中预测野火的深度学习模型
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
论文作者
论文摘要
确定野火可能性很小的地区是土地和林业管理和灾难准备的关键组成部分。我们通过汇总近十年的遥感数据和历史火记录来创建数据集，以预测野火。这个预测问题被构成三个机器学习任务。比较结果并分析了四种不同的深度学习模型，以估计野火的可能性。结果表明，深度学习模型可以使用有关植被，天气和地形的汇总数据成功地识别高火可能性的领域，而AUC为83％。
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%.
