More information about the distribution line data in Transect Vision
Transect provides a dataset called the Derived Map Of Global Electricity Transmission And Distribution Lines, From Paper: Predictive Mapping Of The Global Power System Using Open Data.
The purpose of the dataset is to answer an important question in the quest for universal access to electricity: where is the existing electricity infrastructure? This dataset is a collaboration between ESMAP, Facebook, KTH, WRI and the University of Massachusetts Amherst. Together, they created an algorithm for estimating the location of existing medium-voltage infrastructure.
This data predicts the locations of existing infrastructure and connections. The model uses "night lights" seen on satellite imagery taken at nearly every point on earth monthly around 1:30am. The night light images are then filtered to highlight particularly bright surroundings, and then refined by combining other satellite data to include only areas with human activity. The model also assumes that electric lines likely parallel roads.
According to the paper:
[The] tool applies multiple filtering algorithms to night-time light imagery to identify locations most likely to be producing light from electricity. These light sources (target-locations) are then connected to known electricity networks through a least-cost routing algorithm following roads and known distribution lines (adopted from OpenStreetMap). This results in connected networks at two voltage levels we define as high voltage (HV, >70 kV) and medium voltage (MV, 10–70 kV).
The dataset is then validated by comparing the model data to 16 electricity networks across 14 countries representing High, Upper-Middle, Lower-Middle, and Low income groupings. Across an equal-area grid (edge length 15 km), it was found that the model showed a predictive accuracy of 75%.
As this is modeled data, it is a fairly accurate first assessment of electric line locations, but it is not a substitute for more locally-sourced GIS data or field work. Further, as the data is modeled, more specific information like owner contact and voltage is not provided.
Arderne, C., Zorn, C., Nicolas, C. et al. Predictive mapping of the global power system using open data. Sci Data 7, 19 (2020). https://doi.org/10.1038/s41597-019-0347-4
Antoine, R., Arderne, C., Rogate, C. Using night lights to map electrical grid infrastructure. World Bank. April 29, 2019. https://blogs.worldbank.org/energy/using-night-lights-map-electrical-grid-infrastructure