In the dynamic field of urban planning and the context of unprecedented natural events, such as hurricanes, the fast generation of accurate maps from satellite imagery is paramount. While several studies have utilized Generative Adversarial Networks (GANs) for map generation from satellite images, the present work introduces a new approach by integrating contrastive learning into the GAN framework for enhanced map synthesis. Our methodology distinctively employs positive sampling by aligning similar features (e.g., roads) in both satellite images and their corresponding map outputs, and contrasts this with negative samples for disparate elements. This approach effectively replaces the conventional cyclic process in GANs with a more streamlined, unidirectional procedure, leading to improvements in both the quality of the synthesized maps and computational efficiency. We show the effectiveness of our proposed model, offering an advancement in map generation for remote sensing applications.
