mlsanityData to 7 Oct 2026

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

arXiv 2101.06390 ↗First posted 16 Jan 2021cs.CVcited by 3

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Abstract (arXiv)

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and connectivity of electricity transmission and distribution towers, termed the power grid, is often incomplete, outdated, or altogether unavailable.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 (263km2) 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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 49 references that are in the map. First posted on arXiv 16 Jan 2021.

Builds on

21 of 49 references.

References · 21

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9 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 2.

3 papers.

  1. 2023SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensingcited by 123
  2. 2024Improving satellite imagery segmentation using multiple Sentinel-2 revisitscited by 1
  3. 2024PGRID: Power Grid Reconstruction in Informal Developments Using High-Resolution Aerial Imagerycited by 0