This project focuses on leveraging Generative Adversarial Networks (GANs) for improving satellite imagery analysis by detecting clouds and reconstructing cloud-obstructed regions. Clouds often conceal critical details in satellite images, hindering accurate environmental monitoring and data interpretation. By utilizing GANs, the project aims to develop a robust framework for automatic cloud detection and reconstruction of the obstructed regions. This approach will enhance the quality and usability of satellite data, supporting diverse applications in agriculture, disaster management, and climate studies. The project will involve training GAN models on diverse satellite datasets to achieve high accuracy in both cloud detection and reconstruction tasks.