CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of
  Clouds
Muhammad Ahmed Chaudhry Lyna Kim Jeremy Irvin Yuzu Ido Sonia Chu Jared Thomas Isobe Andrew Y. Ng Duncan Watson-Parris
Clouds play a significant role in global temperature regulation through their
effect on planetary albedo. Anthropogenic emissions of aerosols can alter the
albedo of clouds, but the extent of this effect, and its consequent impact on
temperature change, remains uncertain. Human-induced clouds caused by ship
aerosol emissions, commonly referred to as ship tracks, provide visible
manifestations of this effect distinct from adjacent cloud regions and
therefore serve as a useful sandbox to study human-induced clouds. However, the
lack of large-scale ship track data makes it difficult to deduce their general
effects on cloud formation. Towards developing automated approaches to localize
ship tracks at scale, we present CloudTracks, a dataset containing 3,560
satellite images labeled with more than 12,000 ship track instance annotations.
We train semantic segmentation and instance segmentation model baselines on our
dataset and find that our best model substantially outperforms previous
state-of-the-art for ship track localization (61.29 vs. 48.65 IoU). We also
find that the best instance segmentation model is able to identify the number
of ship tracks in each image more accurately than the previous state-of-the-art
(1.64 vs. 4.99 MAE). However, we identify cases where the best model struggles
to accurately localize and count ship tracks, so we believe CloudTracks will
stimulate novel machine learning approaches to better detect elongated and
overlapping features in satellite images. We release our dataset openly at
{zenodo.org/records/10042922}.