CODE 94666 ACADEMIC YEAR 2018/2019 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 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; and data representation in a geographic information system (GIS). TEACHING METHODS Class lectures (approximately 32 hours) and laboratory exercizes (approximately 8 hours) 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 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 SEBASTIANO SERPICO Ricevimento: By appointment GABRIELE MOSER Ricevimento: By appointment Exam Board GABRIELE MOSER (President) MATTEO PASTORINO (President) SEBASTIANO SERPICO (President) ANDREA DE GIORGI SILVANA DELLEPIANE ALESSANDRO FEDELI ANDREA RANDAZZO LESSONS 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 Data appello Orario Luogo Degree type Note 08/01/2019 11:00 SAVONA Orale 21/01/2019 10:00 SAVONA Orale 23/01/2019 11:00 SAVONA Orale 08/02/2019 10:00 SAVONA Orale 20/02/2019 11:00 SAVONA Orale 26/02/2019 10:00 SAVONA Orale 10/06/2019 10:00 SAVONA Orale 12/06/2019 10:00 SAVONA Orale 12/06/2019 11:00 SAVONA Orale 03/07/2019 11:00 SAVONA Orale 05/07/2019 10:00 SAVONA Orale 18/07/2019 10:00 SAVONA Orale 23/07/2019 11:00 SAVONA Orale 12/09/2019 11:00 SAVONA Orale 18/09/2019 10:00 SAVONA Orale
