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In an era of public Mars fascination, this book offers an objective presentation of the challenges of crewed Mars missions and discusses scenarios of Mars settlements under scientific, technical, social, economic, ethical and political aspects. With the aim to make the reader comprehend what is plausible and what is at stake, the book tries to clarify misconceptions and half-truths spreading rapidly in the public. The authors argue that approximations and misinformation should be countered for two main reasons. First, to avoid missing out on the benefits that Mars exploration may bring, including major scientific discoveries and an inspiring, federative human endeavor. Second, to remediate dangerous delusions – such as the idea that humanity could be transferred there should the Earth become inhabitable in the near term. In preparation for this book a group of European, world-renowned scientists from fields as diverse as astronomy, planetology, geology, biology, philosophy, or economics, as well as astronauts and science-fiction writers, was gathered to discuss Mars missions ranging from near-term robotic missions, all the way to large-scale settlements and even the feasibility of terraforming. For each, they draw arguments from their domains of expertise to discuss what is feasible and what is desirable. The result provides researchers with an objective review of the field, policy makers with a reference to make informed decisions, and the general public with a tool to form educated opinions.

Machine Learning for Small Bodies in the Solar System provides the latest developments and methods in applications of Machine Learning (ML) and Artificial Intelligence (AI) to different aspects of Solar System bodies, including dynamics, physical properties, and detection algorithms. Offering a practical approach, the book encompasses a wide range of topics, providing both readers with essential tools and insights for use in researching asteroids, comets, moons, and Trans-Neptunian objects. The inclusion of codes and links to publicly available repositories further facilitates hands-on learning, enabling readers to put their newfound knowledge into practice. Machine Learning for Small Bodies in the Solar System serves as an invaluable reference for researchers working in the broad fields of Solar System bodies; both seasoned researchers seeking to enhance their understanding of ML and AI in the context of Solar System exploration or those just stepping into the field looking for direction on methodologies and techniques to apply ML and AI in their work. - Provides a practical reference to applications of machine learning and artificial intelligence to small bodies in the Solar System - Approaches the topic from a multidisciplinary perspective, with chapters on dynamics, physical properties and software development - Includes code and links to publicly available repositories to allow readers practice the methodology covered