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