MethaNet - an AI-driven approach to quantifying methane point-source emission from high-resolution 2-D plume imagery (Proposals Track)
Siraput Jongaramrungruang (Caltech)
Abstract
Methane (CH4) is one of the most powerful anthropogenic greenhouse gases with a significant impact on global warming trajectory and tropospheric air quality. Quantifying an emission rate of observed CH4 plumes from aerial or satellite images is a critical step for understanding the local distributions and subsequently prioritizing mitigation target sites. However, there exists no method that can reliably predict emission rates from detected plumes in real-time without ancillary data. Here, we trained a convolutional neural network model, called MethaNet, to predict methane point-source emission directly from high-resolution 2-D plume images without relying on other local measurements such as background wind speeds. Our results support the basis for the applicability of using deep learning techniques to quantify CH4 point sources in an automated manner over large geographical areas. MethaNet opens the way for real-time monitoring systems, not only for present and future airborne field campaigns but also for upcoming space-based observations in this decade.