The generated image is not perfect but is very close to the expected image. In this case, we can see that the generated image captures large roads with orange and yellow as well as green park areas. Additionally, images are generated every 10 epochs and compared to the expected target images. Models are saved every 10 epochs and saved to a file with the training iteration number. The same model architecture and configuration described in the paper was used across a range of image translation tasks. The image translation problem involves converting satellite photos to Google maps format, or the reverse, Google maps images to Satellite photos.Īfter preparing dataset, I developed Pix2Pix model. The Pix2Pix model is a type of conditional GAN, or cGAN, where the generation of the output image is conditional on an input, in this case, a source image. I have used the so-called maps dataset used in the Pix2Pix paper. In this project, I developed a Pix2Pix generative adversarial network for image-to-image translation. Satellite Images to real maps with Deep Learning (and reverse)
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