Generating Positions of Map Items for Placement on a Virtual Map

A computer implemented method of training a generator neural network for use in generating positions of map items for placement on a virtual map, the method comprising:

• receiving one or more training examples, each training example comprising:
• (i) map data, comprising one or more channels of position information for a region, (ii) a latent vector, and (iii) at least one ground truth placement of map items;
• for each training example of the one or more training examples:
• generating, with the generator neural network, a generated placement of map items for the training example, comprising processing the map data and the latent vector of the training example;
• determining, with a discriminator neural network, an estimated probability of the generated placement of map items being a generated placement of map items or a ground truth placement of map items, comprising processing:
• (i) the generated placement of map items (ii) the map data of the training example, and (iii) the latent vector of the training example;
• and
• updating parameters of the generator neural network, comprising:
• determining an adversarial loss using the estimated probability;
• and
• updating the parameters of the generator neural network in dependence on the adversarial loss.

A computer-readable medium storing instructions, which when executed by a processor, cause the processor to:

• receive an input at a generator neural network trained for generating positions of map items for placement on a virtual map, the input comprising:
• map data comprising one or more channels of position information for at least a region of the virtual map, said one or more channels including a channel comprising road position information for the region;
• and
• a latent vector encoding a selection of a placement configuration;
• generate, with the generator neural network, a probability of placing a map item for each subregion of a plurality of subregions of the region of the virtual map;
• and
• generate placement data of map items for the map data using the probability for each subregion.