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

TitleGridTracer: Automatic Mapping of Power Grids Using Deep Learning and Overhead Imagery
Publication TypeJournal Article
Year of Publication2022
AuthorsB Huang, J Yang, A Streltsov, K Bradbury, LM Collins, and JM Malof
JournalIeee Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume15
Start Page4956
Pagination4956 - 4970
Date Published01/2022
Abstract

Energy system information 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and connectivity of electricity transmission and distribution towers-termed the power grid-is often incomplete, outdated, or altogether unavailable.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 an deep learning approach. Toward this goal, we develop and publicly release a large dataset (263 km^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.

DOI10.1109/JSTARS.2021.3124519
Short TitleIeee Journal of Selected Topics in Applied Earth Observations and Remote Sensing