OVERVIEW

The teaching unit introduces the key concepts associated with remote sensing for Earth observation in the framework of the applications to renewable energy. Machine learning concepts are also introduced in the context of these applications.

AIMS AND CONTENT

LEARNING OUTCOMES

In the framework of renewable energy, the objective of the unit is to provide the student with basic knowledge about remote sensing data and their analysis through machine learning, with special focus on the mapping of bio/geophysical parameters associated with renewable energy sources, including vegetation biomass, wind velocity field over sea water, solar irradiance, and air surface temperature.

AIMS AND LEARNING OUTCOMES

After the teaching unit, the student shall know basic notions about remote sensing with optical, radar, and laser sensors, and how to exploit the resulting data to map vegetation biomass, wind velocity over sea and ocean surface, solar irradiance, and air surface temperature. The student shall also know the basic methodological bases of machine learning for supervised classification and regression.

PREREQUISITES

There are no specific requirements, in addition to the normal bases of mathematics and physics that the students are supposed to have from their B.Sc. backgrounds in engineering.

TEACHING METHODS

Class lectures and software laboratory exercises.

Students with a certified learning disability (DSA), a disability, or other special educational needs are invited to contact the instructor at the beginning of the course to discuss teaching and examination arrangements that, while respecting the learning objectives of the course, take individual learning needs into account and provide appropriate accommodations.
Please also note that requests for exam accommodations or exemptions must be submitted using the form available at https://modulionline.unige.it/richiesta-adattamenti#no-back, to the course teacher, the DIME contact person (federico.scarpa@unige.it), and the relevant office (inclusione.studenti@info.unige.it) at least seven working days before the examination, in accordance with the guidelines available at https://unige.it/disabilita-dsa/richiesta-servizi

SYLLABUS/CONTENT

• Basic notions and terminology about sensors, platforms, and space orbits for Earth observation
• Remote sensing data acquisition through passive, active radar, and active laser sensors
• Vegetation biomass mapping through remote sensing image analysis
• Basics of machine learning for classification and regression from remote sensing data
• Wind field velocity estimation over sea and ocean water through remote sensing
• Solar irradiance and air temperature estimation through remote sensing

RECOMMENDED READING/BIBLIOGRAPHY

• Bishop C., Pattern recognition and machine learning, Springer, 2006
• Campbell J. B. and Wynne R. H., Introduction to remote sensing, Guilford Press, 2011
• Goodfellow I., Bengio Y., and Courville A., Deep learning, MIT Press, 2016
• Hastie T., Tibshirani R., and Friedman J., The elements of statistical learning, Springer, 2008
• Hastie T., Tibshirani R., and Friedman J., Gli elementi dell’apprendimento statistico, Piccin, 2025
• Long D. and Ulaby F. T., Microwave radar and radiometric remote sensing, Artech House, 2015
• Manolakis D. G., Lockwood R. B., and Cooley T. W., Hyperspectral imaging remote sensing, Cambridge University Press, 2016
• Moser G., Analisi di immagini telerilevate per osservazione della Terra, ECIG, 2007
• Moser G. and Zerubia J. (eds.), Mathematical models for remote sensing image processing, Springer, 2018
• Sonka M., Hlavac V., Boyle R., Image processing, analysis, and machine vision, Cengage Learning, 2015
• Class slides will be provided to the students through AulaWeb.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/en/corsi/11917/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Mandatory written examination about the topics in the syllabus of the teaching unit, with maximum admissible mark equal to 24/30. If a student obtains a sufficient mark in this written exam, then he/she can also optionally take an additional oral examination with maximum admissible mark equal to 30/30 with honors.

ASSESSMENT METHODS

Within the mandatory written examination, the student's knowledge of the main concepts discussed in the teaching unit shall be evaluated. Within the optional oral examination, the student's capability to address simple problems of remote sensing image analysis in energy applications and his/her capacity to critically discuss the related methodological bases shall be assessed.

FURTHER INFORMATION

Ask the professor for other information not included in the teaching schedule.