Today we get an answer thanks to the work of Xueqing Deng and colleagues at the University of California, Merced. These guys have trained a machine-learning algorithm to create ground-level images simply by looking at satellite pictures from above.
The technique is based on a form of machine intelligence known as a generative adversarial network. This consists of two neural networks called a generator and a discriminator.
The generator creates images that the discriminator assesses against some learned criteria, such as how closely they resemble giraffes. By using the the output from the discriminator, the generator gradually learns to produce images that look like giraffes.
In this case, Deng and co trained the discriminator using real images of the ground as well as satellite images of that location. So it learns how to associate a ground-level image with its overhead view.
Of course, the quality of the data set is important. The team use as ground truth the LCM2015 ground-cover map, which gives the class of land at a one-kilometer resolution for the entire UK. Read more from technologyreview.com…
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