论文标题
Firesrnet：地球科学驱动的超级分辨率是气候变化的未来火灾风险
FireSRnet: Geoscience-Driven Super-Resolution of Future Fire Risk from Climate Change
论文作者
论文摘要
近年来，随着全球越来越频繁和严重的火灾，了解气候变化在火灾行为中的作用对于量化当前和未来的火灾风险至关重要。但是，全球气候模型通常在空间尺度上模拟火灾行为，无法粗糙，无法进行局部风险评估。因此，我们提出了一种新型的超分辨率（SR）增强火灾风险暴露地图的方法，该图不仅包括2000年至2020年的每月卫星观测活动，还包括有关土地覆盖和温度的本地信息。受SR体系结构的启发，我们提出了一个有效的深度学习模型，该模型在火灾风险曝光图上为SR培训。我们在分辨率增强方面评估了该模型，并发现在4倍和8倍增强的同时，在2倍增强时的性能优于标准图像插值技术。然后，我们证明了这种SR模型在北加州和澳大利亚新南威尔士州的普遍性。最后，我们在2040年和2100年将我们提出的模型讨论和应用在气候模型模拟火灾风险上的应用，这说明了从最新最新的气候模型中增强火灾风险图的SR的潜力。
With fires becoming increasingly frequent and severe across the globe in recent years, understanding climate change's role in fire behavior is critical for quantifying current and future fire risk. However, global climate models typically simulate fire behavior at spatial scales too coarse for local risk assessments. Therefore, we propose a novel approach towards super-resolution (SR) enhancement of fire risk exposure maps that incorporates not only 2000 to 2020 monthly satellite observations of active fires but also local information on land cover and temperature. Inspired by SR architectures, we propose an efficient deep learning model trained for SR on fire risk exposure maps. We evaluate this model on resolution enhancement and find it outperforms standard image interpolation techniques at both 4x and 8x enhancement while having comparable performance at 2x enhancement. We then demonstrate the generalizability of this SR model over northern California and New South Wales, Australia. We conclude with a discussion and application of our proposed model to climate model simulations of fire risk in 2040 and 2100, illustrating the potential for SR enhancement of fire risk maps from the latest state-of-the-art climate models.
