  Energy system information valuable for electricity access planning such as
the locations and connectivity of electricity transmission and distribution
towers, termed the power grid, is often incomplete, outdated, or altogether
unavailable. Furthermore, conventional means for collecting this information is
costly and limited. We propose to automatically map the grid in overhead
remotely sensed imagery using deep learning. Towards this goal, we develop and
publicly-release a large dataset ($263km^2$) of overhead imagery with ground
truth for the power grid, to our knowledge this is the first dataset of its
kind in the public domain. Additionally, we propose scoring metrics and
baseline algorithms for two grid mapping tasks: (1) tower recognition and (2)
power line interconnection (i.e., estimating a graph representation of the
grid). We hope the availability of the training data, scoring metrics, and
baselines will facilitate rapid progress on this important problem to help
decision-makers address the energy needs of societies around the world.
