"Surface Waterbody Detection from Satellite Imagery Using U-Net: A Deep Learning Framework for Automated Analysis" is a definitive guide for leveraging advanced deep learning techniques to detect and monitor surface water bodies from satellite images. This book introduces the U-Net architecture, a powerful deep learning model, to automate the analysis of satellite imagery, enabling precise detection of waterbodies across diverse terrains and conditions.With a focus on practical applications, the book provides an in-depth walkthrough of the entire process, including data preprocessing, model training, validation, and performance evaluation using real-world datasets. It addresses key challenges such as handling noisy data, low-resolution imagery, and scalability for large-scale analyses.Whether you are a researcher, data scientist, or environmental analyst, this book equips you with the tools and insights needed to tackle real-world problems in environmental monitoring, disaster management, and resource planning. With detailed code examples, practical case studies, and a look into emerging trends, it empowers you to harness the potential of AI in satellite imagery analysis.