Introduction to Machine Learning for EO

This online course offers an insightful exploration into the application of Machine Learning (ML) within the field of Earth Observation (EO). It begins by examining the current state of ML for EO, highlighting recent advancements and addressing critical ethical considerations pertinent to the domain. The curriculum is structured to provide a comprehensive understanding of how ML principles are applied to environmental science, ensuring participants grasp both theoretical foundations and practical implications.

The course is designed for a diverse audience, including scientists with backgrounds in computer science who are looking to apply Artificial Intelligence (AI) solutions to EO challenges. It also caters to students seeking to expand their knowledge and seasoned professionals aiming to stay current with technological advancements in geospatial AI. Regardless of prior experience, the course aims to provide valuable background information, foster a deeper understanding of ML principles, and showcase real-world applications relevant to various interests and expertise. A basic understanding of both Machine Learning and Earth Observation is advantageous for participants.

Throughout the seven-hour course, learners will acquire essential ML skills specifically tailored for EO research, enhancing their proficiency in ML-driven investigations within the geospatial domain. The content is conveniently divided into smaller segments across six lessons, each building on previous knowledge and presented by acclaimed experts in the AI4EO field. Key topics include an introduction to AI and data science in Earth Observation, and a deep dive into identifying ethical issues and opportunities within AI4EO research, ensuring a well-rounded educational experience.