This book, Artificial Intelligence and Machine Learning in Satellite: Data Processing and Services, presents the selected proceedings of the International Conference on Small Satellites (ICSS 2022) that aims to provide an opportunity for academicians, scientists, researchers, and industry experts, engaged in teaching, research, and development on satellite data processing and its services by employing advanced artificial intelligence-based machine learning techniques. This book covers the application of artificial intelligence and machine learning techniques in various domains of earth observations like natural resources and environmental management, water resources, urban and rural development, climate change, and other contemporary subjects. The book will surely be a valuable asset for beginners, researchers, and professionals working in satellite data processing and services using artificial intelligence and machine learning approaches.

The main objective of this book is to provide a common platform for diverse concepts in satellite image processing. In particular it presents the state-of-the-art in Artificial Intelligence (AI) methodologies and shares findings that can be translated into real-time applications to benefit humankind. Interdisciplinary in its scope, the book will be of interest to both newcomers and experienced scientists working in the fields of satellite image processing, geo-engineering, remote sensing and Artificial Intelligence. It can be also used as a supplementary textbook for graduate students in various engineering branches related to image processing.

The new age space value chain is a complex interconnected system with diverse actors, which involves cross-sector and cross-border collaborations. This book helps to enrich the knowledge of Artificial Intelligence (AI) across the value chain in the space-related domains. Advancements of AI and Machine Learning have impactfully supported the space sector transformation as it is shown in the book. "This book embarks on a journey through the fascinating realm of AI in space, exploring its profound implications, emerging trends, and transformative potential." Prof. Dr. Oliver Ullrich - Director Innovation Cluster Space and Aviaton (UZH Space Hub), University of Zurich, Switzerland Aimed at space engineers, risk analysts, policy makers, technical experts and non-specialists, this book demonstrates insights into the implementation of AI in the space sector, alongside its limitations and use-case examples. It covers diverse AI-related topics applicable to space technologies or space big data such as AI-based technologies for improving Earth Observation big data, AI for space robotics exploration, AI for astrophysics, AI for emerging in-orbit servicing market, and AI for space tourism safety improvement. Key Features: Provides an interdisciplinary approach, with chapter contributions from expert teams working in the governmental or private space sectors, with valuable contributions from computer scientists and legal experts Presents insights into AI implementation and how to unlock AI technologies in the field Up-to-date with the latest developments and cutting-edge applications

Advances in Machine Learning and Image Analysis for GeoAI provides state-of-the-art machine learning and signal processing techniques for a comprehensive collection of geospatial sensors and sensing platforms. The book covers supervised, semi-supervised and unsupervised geospatial image analysis, sensor fusion across modalities, image super-resolution, transfer learning across sensors and time-points, and spectral unmixing among other topics. The chapters in these thematic areas cover a variety of algorithmic frameworks such as variants of convolutional neural networks, graph convolutional networks, multi-stream networks, Bayesian networks, generative adversarial networks, transformers and more.Advances in Machine Learning and Image Analysis for GeoAI provides graduate students, researchers and practitioners in the area of signal processing and geospatial image analysis with the latest techniques to implement deep learning strategies in their research. - Covers the latest machine learning and signal processing techniques that can effectively leverage multimodal geospatial imagery at scale - Chapters cover a variety of algorithmic frameworks pertaining to GeoAI, including superresolution, self-supervised learning, data fusion, explainable AI, among others - Presents cutting-edge deep learning architectures optimized for a wide array of geospatial imagery

INTELLIGENT SATELLITE DESIGN AND IMPLEMENTATION Integrate cutting-edge technology into spacecraft design with this groundbreaking work Artificial intelligence and machine learning have revolutionized virtually every area of computing and complex engineering, and the design of satellite spacecraft is no exception. Intelligent satellites are increasingly capable of human-like perception, decision-making, and operations, and their problem-solving capacities are still expanding. As AI and machine learning continue to advance, their integration into satellite manufacture will only deepen. Intelligent Satellite Design and Implementation seeks to understand the foundations of this integration and its likely directions in the coming years. Beginning from the basic principles of interaction between artificial intelligence and satellite design and mission planning, the book analyzes a series of current or potential areas of technological advancement to create a comprehensive overview of the subject. Intelligent Satellite Design and Implementation readers will also find: Background information on the introduction and development of artificial intelligence Detailed discussion of topics including autonomous satellite operation, remote sensing satellites, and many more Over 100 illustrations and tables to reinforce key concepts Intelligent Satellite Design and Implementation is ideal for graduate students and advanced undergraduates in engineering, computing, and spacecraft design programs, as well as researchers in these and related fields.

This book explores cutting-edge methods combining geospatial technologies and artificial intelligence related to several fields such as smart farming, urban planning, geology, transportation, and 3D city models. It introduces techniques which range from machine and deep learning to remote sensing for geospatial data analysis. The book consists of two main parts that include 13 chapters contributed by promising authors. The first part deals with the use of artificial intelligence techniques to improve spatial data analysis, whereas the second part focuses on the use of artificial intelligence with remote sensing in various fields. Throughout the chapters, the interest for the use of artificial intelligence is demonstrated for different geospatial technologies such as aerial imagery, drones, Lidar, satellite remote sensing, and more. The work in this book is dedicated to the scientific community interested in the coupling of geospatial technologies and artificial intelligence and exploring the synergetic effects of both fields. It offers practitioners and researchers from academia, the industry and government information, experiences and research results about all aspects of specialized and interdisciplinary fields on geospatial intelligence.

This book comprehensively discusses basic data-driven intelligent systems, the methods for processing the data, and cloud computing with artificial intelligence. It presents fundamental and advanced techniques used for handling large user data, and for the data stored in the cloud. It further covers data-driven decision-making for smart logistics and manufacturing systems, network security, and privacy issues in cloud computing. This book: Discusses intelligent systems and cloud computing with the help of artificial intelligence and machine learning. Showcases the importance of machine learning and deep learning in data-driven and cloud-based applications to improve their capabilities and intelligence. Presents the latest developments in data-driven and cloud applications with respect to their design and architecture. Covers artificial intelligence methods along with their experimental result analysis through data processing tools. Presents the advent of machine learning, deep learning, and reinforcement technique for cloud computing to provide cost-effective and efficient services. The text will be useful for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer engineering, manufacturing engineering, and production engineering.