Recently, generative adversarial networks (GANs) are a prominent approach to solving real-time problems. Several domains, like remote sensing, are challenging to handle with conventional methods. The remotely sensed dataset has multiple dimensions that need analysis for better decision-making and future predictions. However, the traditional techniques must be revised to analyse the complex remote sensing data, leading to confused outcomes. In this case, remotely sensed datasets can successively be fused and analysed to address complex challenges in geospatial data and their applications using generative adversarial networks with high success rates. Therefore, the proposed book “Generative Adversarial Networks for Remote Sensing: Trends and Applications” emphasises the foundations of recent trends in generative adversarial networks and remote sensing applications. Firstly, the book will provide insights into the fundamentals of generative adversarial networks, historical advancements, novel GAN architectures and challenges in analysing remote sensing data using GANs. The book will also focus on feature extraction, object detection and segmentation from remotely sensed data with GANs to interpret geospatial surface features. Subsequently, the book will focus on practical executions of GANs for the time series analysis of remote sensing data for detecting the real-time changes in geolocations, fusion of multiple remote sensing datasets, and processing multi-model data. The book will also focus on real-time case studies on remote sensing applications for precision agriculture, environmental changes and monitoring, smart cities and their planning, and forestry applications using the GANs across varied fields.