The course aims to provide fundamentals of Remote Sensing techniques and applications for Earth Observation in the optical domain. The objective of the course is to teach students the basics of remote sensing using instruments on satellite, airborne, drone and ground-based set-ups. State-of-the-art semi-empirical regression and physical-based model inversion are discussed together with novel AI based approaches.

1. Knowledge and understanding.
The student will acquire a solid understanding of:
-the physical principles underlying Earth remote sensing,
-characteristics and types of remote sensing systems, spatial/spectral resolutions, and data acquisition platforms (satellites, aircraft, drones),
-major space programs (e.g., Copernicus) and their data products,
-representation and interpretation of multispectral and hyperspectral imagery,
-pre-processing techniques (radiometric, spectral, geometric corrections; atmospheric correction),
-the fundamentals of digital image enhancement, statistical analysis, and AI-based processing methods,
-applications of spectral indices and bio-geophysical parameter extraction,
-creation of thematic maps and geospatial data visualization across scales.

2. Applying knowledge and understanding.
Upon completion of the course, students will be able to:
-handle and pre-process remote sensing data from multiple sources (satellite, aerial, drone),
-apply correction techniques to improve image quality and comparability,
-use image classification algorithms (including AI-based methods) for tasks such as land use and vegetation mapping,
-compute and interpret spectral indices for environmental monitoring,
-retrieve bio-geophysical parameters and assess their spatial variability,
-generate thematic maps for practical applications in agriculture, forestry, inland water monitoring, and geology,
-work with multi-scale geospatial data and integrate them into environmental decision-making.

3. Making judgements.
Students will develop the ability to:
-critically evaluate the suitability of different remote sensing techniques and processing methods for specific environmental applications,
-assess the quality and limitations of remote sensing data,
-select appropriate processing workflows for extracting meaningful information depending on the spatial and spectral characteristics of the data,
-judge the reliability and significance of thematic products derived from remote sensing imagery.

4. Communication skills.
Students will be able to:
-clearly communicate remote sensing concepts, methods, and results using appropriate scientific and technical terminology,
-present geospatial data and thematic maps in a clear and interpretable format,
-prepare concise reports and visual materials for both technical and non-technical audiences.

5. Learning skills.
Students will develop:
-the ability to independently learn new tools and techniques in remote sensing and AI-based image processing,
-skills to consult scientific literature and technical documentation related to environmental Earth observation,
-readiness to engage in advanced studies or professional roles in geospatial analysis, environmental science, or data-driven decision support.