Aerial Image and Map Synthesis Using Generative Adversarial Networks

Accurate automatic conversion between aerial images and maps is a valuable and challenging task in computer vision and computer graphics. Deep convolutional neural networks (CNN) have achieved promising results on this task but the results accuracy is not ideal. In this paper, we propose a solution to improve the precision and quality of the transforming results. The core learning method is based on generative adversarial networks (GANs). A novel generator and a multi-scale discriminator are introduced in our network. The generator operates at the progressive method to gurantee the spatial consistency between the inputs and outputs, and our multi-scale discriminator focuses on increasing the capacity of the network and guides the generator to generate better results. In particular, our architecture can also be used as a general neural network for style translation. Analytic experiments on the aerial-to-map dataset show that our network outperforms the existing method, advancing both accuracy and visual appearance.