REMOTE SENSING OF NATURAL DISASTERS

REMOTE SENSING OF NATURAL DISASTERS

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iten
Code
94666
ACADEMIC YEAR
2018/2019
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
Teaching materials

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

Ricevimento: By appointment

Ricevimento: By appointment

Exam Board

SEBASTIANO SERPICO (President)

MATTEO PASTORINO (President)

GABRIELE MOSER (President)

SILVANA DELLEPIANE

ANDREA RANDAZZO

ALESSANDRO FEDELI

ANDREA DE GIORGI

LESSONS

Teaching methods

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

ORARI

L'orario di tutti gli insegnamenti è consultabile su EasyAcademy.

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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.