Power grid assets prediction using generative adversarial networks

A method for using a neural network to predict locations of feeders in an electrical power grid, the method comprising:

• training a generative adversarial network comprising a generator and a discriminator, wherein training the generative adversarial network comprises:
• training the generator while holding the discriminator fixed, including providing training input to the generator, the training input comprising training input map data, and
• providing corresponding training outputs generated by the generator to the discriminator, the training outputs comprising map data including feeder metadata, and training the generator based on a respective discriminator output from the discriminator for each training output received by the discriminator;
• and
• generating, by the generator, from input map data including input images, output map data with feeder metadata that represents predicted locations of feeder assets, including receiving, by the generator, a first input image and generating by the generator corresponding first output map data with first feeder data that identifies one or more feeder assets and their respective locations, wherein the one or more feeder assets had not been identified in any input to the generator.