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 and shall be able to apply, through dedicated software platforms, 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).

TEACHING METHODS

Class lectures (approximately 32 hours) and laboratory exercizes (approximately 8 hours). In relation to the COVID-19 pandemic and to the resulting travel restrictions and social distancing measures, classes will be given telematically and software exercizes will be organized online through the use of open software.

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

RECOMMENDED READING/BIBLIOGRAPHY

• Richards J. A., Remote sensing digital image analysis, Springer, 2013
• 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
• Moser G., Analisi di immagini telerilevate per osservazione della Terra, ECIG, 2007
• 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

MATTEO PASTORINO (President Substitute)

ANDREA RANDAZZO (President Substitute)

SEBASTIANO SERPICO (President Substitute)

LESSONS

LESSONS START

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

Class schedule

REMOTE SENSING OF NATURAL DISASTERS

EXAMS

EXAM DESCRIPTION

Oral examination

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