  Humans excel at detecting interesting patterns in images, for example those
taken from satellites. This kind of anecdotal evidence can lead to the
discovery of new phenomena. However, it is often difficult to gather enough
data of subjective features for significant analysis. This paper presents an
example of how two tools that have recently become accessible to a wide range
of researchers, crowd-sourcing and deep learning, can be combined to explore
satellite imagery at scale. In particular, the focus is on the organization of
shallow cumulus convection in the trade wind regions. Shallow clouds play a
large role in the Earth's radiation balance yet are poorly represented in
climate models. For this project four subjective patterns of organization were
defined: Sugar, Flower, Fish and Gravel. On cloud labeling days at two
institutes, 67 scientists screened 10,000 satellite images on a crowd-sourcing
platform and classified almost 50,000 mesoscale cloud clusters. This dataset is
then used as a training dataset for deep learning algorithms that make it
possible to automate the pattern detection and create global climatologies of
the four patterns. Analysis of the geographical distribution and large-scale
environmental conditions indicates that the four patterns have some overlap
with established modes of organization, such as open and closed cellular
convection, but also differ in important ways. The results and dataset from
this project suggests promising research questions. Further, this study
illustrates that crowd-sourcing and deep learning complement each other well
for the exploration of image datasets.
