Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA.
The University of Sydney, University Centre for Rural Health, School of Public Health, Sydney, NSW, Australia.
J Expo Sci Environ Epidemiol. 2021 Feb;31(1):170-176. doi: 10.1038/s41370-020-0246-y. Epub 2020 Jul 27.
Wildland fire (wildfire; bushfire) pollution contributes to poor air quality, a risk factor for premature death. The frequency and intensity of wildfires are expected to increase; improved tools for estimating exposure to fire smoke are vital. New-generation satellite-based sensors produce high-resolution spectral images, providing real-time information of surface features during wildfire episodes. Because of the vast size of such data, new automated methods for processing information are required.
We present a deep fully convolutional neural network (FCN) for predicting fire smoke in satellite imagery in near-real time (NRT).
The FCN identifies fire smoke using output from operational smoke identification methods as training data, leveraging validated smoke products in a framework that can be operationalized in NRT. We demonstrate this for a fire episode in Australia; the algorithm is applicable to any geographic region.
The algorithm has high classification accuracy (99.5% of pixels correctly classified on average) and precision (average intersection over union = 57.6%).
The FCN algorithm has high potential as an exposure-assessment tool, capable of providing critical information to fire managers, health and environmental agencies, and the general public to prevent the health risks associated with exposure to hazardous smoke from wildland fires in NRT.
野火（森林大火；林火）污染导致空气质量差，这是导致过早死亡的一个危险因素。预计野火的频率和强度将会增加；改进用于估计火灾烟雾暴露的工具至关重要。新一代基于卫星的传感器可生成高分辨率光谱图像，在野火发生期间提供表面特征的实时信息。由于此类数据的规模庞大，因此需要新的自动化信息处理方法。
我们提出了一种用于近实时（NRT）卫星图像中火灾烟雾预测的深度全卷积神经网络（FCN）。
FCN 使用作为训练数据的运行中烟雾识别方法的输出来识别火灾烟雾，利用经过验证的烟雾产品构建一个可在 NRT 中运行的框架。我们用澳大利亚的一次火灾事件进行了演示；该算法适用于任何地理区域。
该算法具有很高的分类准确性（平均 99.5%的像素正确分类）和精度（平均交并比=57.6%）。
FCN 算法作为暴露评估工具具有很高的潜力，能够为火灾管理人员、卫生和环境机构以及公众提供关键信息，以防止与暴露于 NRT 中野火产生的危险烟雾相关的健康风险。
