High-Resolution Image-to-Image Translation for Aesthetic Enhancements Using Generative Adversarial Network

Urbanization poses numerous challenges in handling cities' growth and increasing populations. This research em-phasizes the significance of architecture enhancement in urban development and the conversion of satellite images into clear map representations using generator and discriminator models. Architectural enhancement is crucial for improving both the aesthetic appeal and functionality of buildings. Architectural enhancement through Generative Adversarial Networks (GANs) is a transformative approach to renovating old structures. This method facilitates the preservation of original design aesthetics, allowing for virtual restoration and the recreation of missing elements while ensuring accuracy. Concurrently, converting satel-lite images to precise maps aids city planning by providing a comprehensive understanding of spatial layouts and facilitating accurate land-use analysis. Hence, employing GANs enables the generation of high-resolution, realistic images that support object manipulation and diverse outcomes from a single input. This method not only enhances image synthesis techniques but also unlocks numerous applications in urban planning and design.