Paper Title
Cloud Detection and Segmentation in Remote Sensing Imagery

Abstract
The continuous stream of data generated by advanced Earth observation satellites serves as a critical resource for monitoring vegetation health, water quality, disaster management, and other environmental applications. However, the presence of clouds and fog significantly limits the usability of multispectral remote sensing images, rendering a substantial portion of the data ineffective. Addressing this challenge requires advanced computational techniques that can effectively restore obscured regions in satellite imagery.This study demonstrates the effectiveness of Generative Adversarial Networks (GANs) for removing clouds and fog from Landsat multispectral images. By leveraging deep learning techniques, GANs can reconstruct occluded areas, enabling seamless data continuity. The proposed method enhances the quality of satellite imagery, ensuring uninterrupted datasets for scientific and commercial applications. The findings highlight the potential of GAN-based approaches in improving remote sensing data usability, thereby contributing to better decision-making in environmental monitoring, land-use planning, and disaster response. Keywords - Remote Sensing, Cloud Removal, Fog Removal, Generative Adversarial Networks (GANs), Deep Learning, Satellite Imagery, Image Restoration, Multispectral Data, Environmental Monitoring, Land-Use Planning.