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Energy system information valuable for electricity access planning such asthe locations and connectivity of electricity transmission and distributiontowers, termed the power grid, is often incomplete, outdated, or altogetherunavailable. Furthermore, conventional means for collecting this information iscostly and limited. We propose to automatically map the grid in overheadremotely sensed imagery using deep learning. Towards this goal, we develop andpublicly-release a large dataset ($263km^2$) of overhead imagery with groundtruth for the power grid, to our knowledge this is the first dataset of itskind in the public domain. Additionally, we propose scoring metrics andbaseline algorithms for two grid mapping tasks: (1) tower recognition and (2)power line interconnection (i.e., estimating a graph representation of thegrid). We hope the availability of the training data, scoring metrics, andbaselines will facilitate rapid progress on this important problem to helpdecision-makers address the energy needs of societies around the world.

GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery