Super-Resolution of BVOC Maps by Adapting Deep Learning Methods
This work addresses the need for fine-grained BVOC data in environmental science, but it is incremental as it applies known neural network techniques to a specific domain without introducing new methods.
The paper tackled the problem of generating high-resolution Biogenic Volatile Organic Compound (BVOC) emission maps, which are costly to acquire, by adapting existing deep learning super-resolution methods to handle large dynamic ranges and outliers, achieving enhanced predictions for applications like air quality and climate monitoring.
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.