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 module, 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 representation of thematic products 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 software 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
• GIS tools for data and thematic product representation

This module 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

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

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 module shall be evaluated. Within the optional oral examination, the student's capability to address simple problems of remote sensing, in applications to natural disaster management, and his/her capacity to critically discuss the related methodological bases shall be assessed.