通过适配深度学习方法实现BVOC地图的超分辨率
Super-Resolution of BVOC Maps by Adapting Deep Learning Methods
AI总结：
本研究适配多种最先进的图像超分辨率神经网络，以克服排放动态范围大和异常值影响，在兼顾时空约束下实现高分辨率BVOC排放地图的重建。
摘要：
生物源挥发性有机化合物（BVOCs）在生物圈-大气相互作用中起着至关重要的作用，是影响大气物理化学性质和气候的关键因素。获取大范围且精细的BVOC排放地图成本高昂且耗时，因此大多数可用的BVOC数据都是在稀疏的采样网格上或小区域内获取的。然而，高分辨率BVOC数据在许多应用中是亟需的，如空气质量、大气化学和气候监测。在本研究中，我们探讨了增强BVOC获取的可能性，并进一步解释了环境与这些化合物之间的关系。我们通过比较几种用于图像超分辨率（SR）的最先进神经网络的性能来实现这一点，并对它们进行适配，以克服排放的大动态范围带来的挑战，并减少异常值对预测的影响。此外，我们还考虑了现实场景，兼顾了时间和地理约束。最后，我们提出了关于SR泛化的未来可能发展方向，考虑了尺度不变性属性以及对未见化合物的排放进行超分辨率处理。
英文摘要：
Biogenic Volatile Organic Compounds (BVOCs) play a critical role in biosphere-atmosphere interactions, being a key factor in the physical and chemical properties of the atmosphere and climate. Acquiring large and fine-grained BVOC emission maps is expensive and time-consuming, so most available BVOC data are obtained on a loose and sparse sampling grid or on small regions. However, high-resolution BVOC data are desirable in many applications, such as air quality, atmospheric chemistry, and climate monitoring. In this work, we investigate the possibility of enhancing BVOC acquisitions, further explaining the relationships between the environment and these compounds. We do so by comparing the performances of several state-of-the-art neural networks proposed for image Super-Resolution (SR), adapting them to overcome the challenges posed by the large dynamic range of the emission and reduce the impact of outliers in the prediction. Moreover, we also consider realistic scenarios, considering both temporal and geographical constraints. Finally, we present possible future developments regarding SR generalization, considering the scale-invariance property and super-resolving emissions from unseen compounds.
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