This course aims to equip students with a comprehensive understanding of advanced signal and image processing techniques tailored for environmental applications. By bridging the gap between complex remote sensing technologies –such as remote observations, hyperspectral imaging and satellite observations– and state-of-the-art Artificial Intelligence, the course prepares students to analyze high-resolution data and extract meaningful insights. Ultimately, students will develop the critical judgment, communication, and autonomous learning skills required to build, evaluate, and articulate AI-driven solutions for modern environmental challenges. Upon successful completion of this course, students will be able to:

1. Knowledge and understanding: Demonstrate an in-depth understanding of signal and image acquisition and analysis techniques relevant to environmental applications, focusing on advanced methodologies such as remote sensing, hyperspectral imaging, and satellite or telescope-based observation systems.

2. Applying knowledge and understanding: Apply signal and image processing techniques to high-resolution data acquired through complex remote sensing systems, extracting meaningful features using state-of-the-art Machine Learning and Artificial Intelligence tools

3. Making judgments: Critically assess the quality and reliability of acquired environmental data, select appropriate AI models and processing pipelines, and interpret results in light of application-specific constraints and objectives.

4. Communication skills: Effectively communicate complex signal and image analysis processes and their results through technical reports, visual presentations, and interdisciplinary collaboration, utilizing precise terminology and advanced data visualization tools.

5. Learning skills: Autonomously adapt to the rapidly evolving fields of environmental sensing, imaging technologies, and AI-based data analysis, demonstrating the ability to keep pace with emerging tools, methodologies, and scientific literature.