  Change detection methods applied to monitoring key infrastructure like
airport runways represent an important capability for disaster relief and urban
planning. The present work identifies two generative adversarial networks (GAN)
architectures that translate reversibly between plausible runway maps and
satellite imagery. We illustrate the training capability using paired images
(satellite-map) from the same point of view and using the Pix2Pix architecture
or conditional GANs. In the absence of available pairs, we likewise show that
CycleGAN architectures with four network heads (discriminator-generator pairs)
can also provide effective style transfer from raw image pixels to outline or
feature maps. To emphasize the runway and tarmac boundaries, we experimentally
show that the traditional grey-tan map palette is not a required training input
but can be augmented by higher contrast mapping palettes (red-black) for
sharper runway boundaries. We preview a potentially novel use case (called
"sketch2satellite") where a human roughly draws the current runway boundaries
and automates the machine output of plausible satellite images. Finally, we
identify examples of faulty runway maps where the published satellite and
mapped runways disagree but an automated update renders the correct map using
GANs.
