  Generation of maps from satellite images is conventionally done by a range of
tools. Maps became an important part of life whose conversion from satellite
images may be a bit expensive but Generative models can pander to this
challenge. These models aims at finding the patterns between the input and
output image. Image to image translation is employed to convert satellite image
to corresponding map. Different techniques for image to image translations like
Generative adversarial network, Conditional adversarial networks and
Co-Variational Auto encoders are used to generate the corresponding
human-readable maps for that region, which takes a satellite image at a given
zoom level as its input. We are training our model on Conditional Generative
Adversarial Network which comprises of Generator model which which generates
fake images while the discriminator tries to classify the image as real or fake
and both these models are trained synchronously in adversarial manner where
both try to fool each other and result in enhancing model performance.
