Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

中文翻译：

---

一种深度学习的静止地球静止卫星影像的深度支持方法，用于大火力野火的决策支持

荒地火灾进展数据的稀缺以及预报中的大量不确定性，需要改进的方法来实时监控火灾蔓延。但是，目前还没有可伸缩的解决方案来获取有关在空间和时间上都是明确的活动森林火灾的一致信息。为了克服此限制，我们提出了一种基于深度学习的统计缩减规模方案，该方案利用了多源遥感（RS）数据。我们的系统依靠U-Net卷积神经网络（CNN）缩减对地静止（GEO）卫星多光谱图像，并以类似于低地球轨道（LEO）传感器的空间分辨率连续监视主动火的进程。为了实现这一目标，该模型对LEO RS产品，土地使用信息，植被属性和地形数据进行了训练。已对实际实施进行了优化，以使用云计算集群，软件容器和多步并行管道，以促进实时操作部署。该模型的性能在2017年和2018年发生于加利福尼亚的最具破坏性的5次野火中得到了验证。这些结果证明了所提出方法在以高时空分辨率监控火势进展中的有效性，这可以为决策支持提供帮助在野火的头几个小时可能会迅速变得大而危险。另外，可以利用所提出的方法来收集有关真实规模野火行为的详细定量数据，从而支持火势蔓延模型的开发和验证。