CODE 94666 ACADEMIC YEAR 2024/2025 CREDITS 5 cfu anno 2 ENGINEERING FOR NATURAL RISK MANAGEMENT 10553 (LM-26) - SAVONA SCIENTIFIC DISCIPLINARY SECTOR ING-INF/03 LANGUAGE English TEACHING LOCATION SAVONA SEMESTER 1° Semester MODULES Questo insegnamento è un modulo di: REMOTE SENSING AND ELECTROMAGNETIC TECHNIQUES FOR RISK MONITORING TEACHING MATERIALS AULAWEB 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 SEBASTIANO SERPICO Ricevimento: By appointment GABRIELE MOSER Ricevimento: By appointment. STEFANIA TRAVERSO 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. Agenda 2030 - Sustainable Development Goals Quality education Decent work and economic growth Climate action Life on land