GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery
This work addresses the energy access planning problem for decision-makers by providing automated tools, though it is incremental as it introduces a new dataset and baselines rather than a breakthrough method.
The authors tackled the problem of incomplete and costly power grid mapping by developing a deep learning approach using overhead imagery, resulting in the creation of a large public dataset (263km²) and baseline algorithms for tower recognition and grid interconnection tasks.
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and connectivity of electricity transmission and distribution towers, termed the power grid, is often incomplete, outdated, or altogether unavailable.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.