All I can think of is image generation of potential targets like ships, airplane, airfield and feed them to their satellite or drones for image detection and tweak their weapons for enhance precision.
I think the usual computer vision wisdom is that this (training object detection on generated imagery) doesn't work very well. But maybe the corps have some techniques that aren't in the public literature yet.
My understanding is the opposite, see papers for "synthetic" data training. They use a small bit if real data to generate lots of synthetic data and get usable results.
The bias leans towards overfitting the data, which in some use cases - such as missile or drone design which doesn't need broad comparisons like 747s or artillery to complete it's training.
Kind of like neural net back propogation but in terms of model /weights
Something I've wanted to comment on this as well. It's best to use render_template() when rendering the views so it doesn't tangle w/ a lot of python controller logic.
