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REMOTE SENSING OF NATURAL DISASTERS

CODE 94666
ACADEMIC YEAR 2019/2020
CREDITS 5 credits during the 2nd year of 10553 ENGINEERING FOR NATURAL RISK MANAGEMENT (LM-26) SAVONA
SCIENTIFIC DISCIPLINARY SECTOR ING-INF/03
LANGUAGE English
TEACHING LOCATION SAVONA (ENGINEERING FOR NATURAL RISK MANAGEMENT)
SEMESTER 1° Semester
MODULES This unit is a module of:
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

Exam Board

GABRIELE MOSER (President)

MATTEO PASTORINO (President)

SEBASTIANO SERPICO (President)

ANDREA DE GIORGI

SILVANA DELLEPIANE

ALESSANDRO FEDELI

LUCA MAGGIOLO

ANDREA RANDAZZO

DAVID SOLARNA

LESSONS

TEACHING METHODS

Class lectures (approximately 32 hours) and laboratory exercizes (approximately 8 hours)

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

Date Time Location Type Notes
08/01/2020 11:00 GENOVA Esame su appuntamento Exam by appointment
29/01/2020 10:00 SAVONA Orale
29/01/2020 11:00 GENOVA Esame su appuntamento Exam by appointment
07/02/2020 10:00 SAVONA Orale
19/02/2020 11:00 GENOVA Esame su appuntamento Exam by appointment
26/02/2020 10:00 SAVONA Orale
08/06/2020 10:00 SAVONA Orale
10/06/2020 11:00 GENOVA Esame su appuntamento Exam by appointment
01/07/2020 11:00 GENOVA Esame su appuntamento Exam by appointment
03/07/2020 10:00 SAVONA Orale
15/07/2020 10:00 SAVONA Orale
22/07/2020 11:00 GENOVA Esame su appuntamento Exam by appointment
09/09/2020 11:00 GENOVA Esame su appuntamento Exam by appointment
16/09/2020 10:00 SAVONA Orale