AIMS AND CONTENT

LEARNING OUTCOMES

The course introduces the key concepts related to information extraction from remote sensing images in the framework of disaster risk prevention and assessment. Basic knowledge will be provided about remote sensing image acquisition through passive sensors; land cover mapping through remote sensing image classification in the application to risk prevention; detection of ground changes from multitemporal remote sensing images in the application to damage assessment; and data representation in a geographic information system (GIS).

AIMS AND LEARNING OUTCOMES

After the course, the student shall know the key concepts related to information extraction from remote sensing images in the framework of disaster risk prevention and assessment. He/she shall have basic knowledge about: remote sensing images; land cover mapping through remote sensing image classification in the application to risk prevention; detection of ground changes from multitemporal remote sensing images in the application to damage assessment; bio/geophysical parameter estimation through regression from remote sensing data; and data representation in a geographic information system (GIS). In this framework, he/she shall also have methodological bases about machine learning for supervised classification and regression. 

TEACHING METHODS

Class lectures and laboratory exercizes.

SYLLABUS/CONTENT

• Basic notions and terminology about sensors, platforms, and space orbits for Earth observation
• Remote sensing image acquisition through passive sensors
• Land cover mapping through remote sensing image classification
• Detection of changes through multitemporal remote sensing image analysis
• Bio/geophysical parameter estimation through regression from remote sensing data
• Data representation in a geographic information system

This course also contributes to the achievement of the following Sustainable Development Goals of the UN 2030 Agenda: Objectives no. 4, 8, 13, and 15.

RECOMMENDED READING/BIBLIOGRAPHY

• Richards J. A., Remote sensing digital image analysis, Springer, 2022
• Bishop C., Pattern recognition and machine learning, Springer, 2006
• Campbell J. B. and Wynne R. H., Introduction to remote sensing, Guilford Press, 2011
• 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

Exam Board

GABRIELE MOSER (President)

SILVANA DELLEPIANE

ALESSANDRO FEDELI

LUCA MAGGIOLO

ROOZBEH RAJABI TOOSTANI

ANDREA RANDAZZO (President Substitute)

SEBASTIANO SERPICO (President Substitute)

LESSONS

LESSONS START

https://courses.unige.it/10553/p/students-timetable

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral examination on the topics included in the syllabus of the course.

Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

ASSESSMENT METHODS

Within the oral examination, the student's knowledge of the course topics and his/her capability to discuss how to address simple problems of remote sensing data analysis associated with disaster risk prevention and management shall be evaluated.

Exam schedule