  At least a quarter of the warming that the Earth is experiencing today is due
to anthropogenic methane emissions. There are multiple satellites in orbit and
planned for launch in the next few years which can detect and quantify these
emissions; however, to attribute methane emissions to their sources on the
ground, a comprehensive database of the locations and characteristics of
emission sources worldwide is essential. In this work, we develop deep learning
algorithms that leverage freely available high-resolution aerial imagery to
automatically detect oil and gas infrastructure, one of the largest
contributors to global methane emissions. We use the best algorithm, which we
call OGNet, together with expert review to identify the locations of oil
refineries and petroleum terminals in the U.S. We show that OGNet detects many
facilities which are not present in four standard public datasets of oil and
gas infrastructure. All detected facilities are associated with characteristics
known to contribute to methane emissions, including the infrastructure type and
the number of storage tanks. The data curated and produced in this study is
freely available at http://stanfordmlgroup.github.io/projects/ognet .
