CODE 94666 ACADEMIC YEAR 2022/2023 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; bio/geophysical parameter estimation through regression methods; 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). 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 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 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 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 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 Data appello Orario Luogo Degree type Note 25/01/2023 10:00 SAVONA Orale 25/01/2023 10:00 SAVONA Orale 15/02/2023 10:00 SAVONA Orale 15/02/2023 10:00 SAVONA Orale 05/06/2023 10:00 SAVONA Orale 05/06/2023 10:00 SAVONA Orale 26/06/2023 10:00 SAVONA Orale 26/06/2023 10:00 SAVONA Orale 13/09/2023 10:00 SAVONA Orale 13/09/2023 10:00 SAVONA Orale