OVERVIEW

The course 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

Introducing the key concepts associated with Earth observation through remote sensing images for renewable energy applications. Providing the students with basic knowledge about remote sensing image acquisition and about mapping, through remote sensing image analysis, 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 course, 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.

TEACHING METHODS

Class lectures and software laboratory exercises.

Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, course work and exams, should speak both with the teacher and with Prof. Federico Scarpa (federico.scarpa@unige.it ), the Department's disability liaison.

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
• Denoising, despeckling, and filtering remote sensing images

This unit also contributes to the achievement of the following Sustainable Development Goals of the UN 2030 Agenda: Objectives no. 4, 7, 8, and 13.

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
• 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/10170/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 course, 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.

Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Prof. Federico Scarpa (federico.scarpa@unige.it), the disability liaison for the Engineering study programs.

ASSESSMENT METHODS

Within the mandatory written examination, the student's knowledge of the main concepts discussed in the course 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.