AIMS AND CONTENT

LEARNING OUTCOMES

The objective of this module is to provide the student with 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 and bio/geophysical parameter retrieval in the application to risk prevention; change detection in the application to damage assessment; and data representation in a geographic information system.

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.

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 DITEN contact person (silvana.dellepiane@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 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

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
• 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/10553/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 module, 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 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.

FURTHER INFORMATION

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