  The major driver of global warming has been identified as the anthropogenic
release of greenhouse gas (GHG) emissions from industrial activities. The
quantitative monitoring of these emissions is mandatory to fully understand
their effect on the Earth's climate and to enforce emission regulations on a
large scale. In this work, we investigate the possibility to detect and
quantify industrial smoke plumes from globally and freely available multi-band
image data from ESA's Sentinel-2 satellites. Using a modified ResNet-50, we can
detect smoke plumes of different sizes with an accuracy of 94.3%. The model
correctly ignores natural clouds and focuses on those imaging channels that are
related to the spectral absorption from aerosols and water vapor, enabling the
localization of smoke. We exploit this localization ability and train a U-Net
segmentation model on a labeled sub-sample of our data, resulting in an
Intersection-over-Union (IoU) metric of 0.608 and an overall accuracy for the
detection of any smoke plume of 94.0%; on average, our model can reproduce the
area covered by smoke in an image to within 5.6%. The performance of our model
is mostly limited by occasional confusion with surface objects, the inability
to identify semi-transparent smoke, and human limitations to properly identify
smoke based on RGB-only images. Nevertheless, our results enable us to reliably
detect and qualitatively estimate the level of smoke activity in order to
monitor activity in industrial plants across the globe. Our data set and code
base are publicly available.
