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The shipping industry is one of the strongest anthropogenic emitters of$\text{NO}_\text{x}$ -- substance harmful both to human health and theenvironment. The rapid growth of the industry causes societal pressure oncontrolling the emission levels produced by ships. All the methods currentlyused for ship emission monitoring are costly and require proximity to a ship,which makes global and continuous emission monitoring impossible. A promisingapproach is the application of remote sensing. Studies showed that some of the$\text{NO}_\text{2}$ plumes from individual ships can visually be distinguishedusing the TROPOspheric Monitoring Instrument on board the Copernicus Sentinel 5Precursor (TROPOMI/S5P). To deploy a remote sensing-based global emissionmonitoring system, an automated procedure for the estimation of$\text{NO}_\text{2}$ emissions from individual ships is needed. The extremelylow signal-to-noise ratio of the available data as well as the absence ofground truth makes the task very challenging. Here, we present a methodologyfor the automated segmentation of $\text{NO}_\text{2}$ plumes produced byseagoing ships using supervised machine learning on TROPOMI/S5P data. We showthat the proposed approach leads to a more than a 20\% increase in the averageprecision score in comparison to the methods used in previous studies andresults in a high correlation of 0.834 with the theoretically derived shipemission proxy. This work is a crucial step toward the development of anautomated procedure for global ship emission monitoring using remote sensingdata.

Supervised segmentation of NO2 plumes from individual ships using TROPOMI satellite data