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

Bohao Huang, Jichen Yang, Artem Streltsov, Kyle Bradbury, Leslie M. Collins, Jordan M. Malof

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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and connectivity of electricity transmission and distribution towers-termed the power grid-is often incomplete, outdated, or altogether unavailable.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.

Original languageEnglish
Pages (from-to)4956-4970
Number of pages15
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
StatePublished - 2022


  • Deep learning
  • Energy systems
  • Object detection
  • Power grid
  • Remote sensing


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