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Percorso della pagina
  1. Science
  2. Master Degree
  3. Artificial Intelligence for Science and Technology [F9103Q - F9102Q]
  4. Courses
  5. A.A. 2024-2025
  6. 2nd year
  1. Physical Sensors and Systems for Environmental Imaging
  2. Summary
Insegnamento Course full name
Physical Sensors and Systems for Environmental Imaging
Course ID number
2425-2-F9102Q021
Course summary SYLLABUS

Course Syllabus

  • Italiano ‎(it)‎
  • English ‎(en)‎
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Obiettivi

Contenuti sintetici

Programma esteso

Prerequisiti

Modalità didattica

Materiale didattico

Periodo di erogazione dell'insegnamento

Modalità di verifica del profitto e valutazione

Orario di ricevimento

Sustainable Development Goals

ISTRUZIONE DI QUALITÁ | PARITÁ DI GENERE | LAVORO DIGNITOSO E CRESCITA ECONOMICA | IMPRESE, INNOVAZIONE E INFRASTRUTTURE
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Aims

The course aims to provide fundamentals of Remote Sensing techniques and applications for Earth Observation in the optical domain. The objective of the course is to teach students the basics of remote sensing using instruments on satellite, airborne, drone and ground-based set-ups. State-of-the-art semi-empirical regression and physical-based model inversion are discussed together with novel AI based approaches.

Contents

The course includes lectures about the fundamental concepts of Remote Sensing techniques applied to Earth Observation and Environmental monitoring. The processing and interpretation of imaging data to quantitatively study the Environment. The course includes applied remote sensing topics aimed at characterizing major Earth surfaces characteristics and processes (e.g., vegetation, snow, water, atmosphere).

Detailed program

REMOTE SENSING FUNDAMENTALS
• Physical principles for Earth Remote Sensing
• Remote sensing systems and resolutions
• Space missions and the Copernicus program
• Multispectral/Hyperspectral image representation and interpretation
• Multi-scale remote sensing (satellite, airborne, drone spectral imaging)

IMAGE PRE-PROCESSING
• Radiometric/spectral/geometric processing
• Atmospheric correction methods

STATE-OF-THE-ART AND AI-BASED IMAGE PROCESSING METHODS
• Digital imaging enhancement and statistical analysis
• Image classification (land use classification)
• Spectral indices
• Bio-geophysical parameters retrieval
• Thematic maps of environmental parameters

MULTI-SCALE GEOSPATIAL MAPPING APPLICATIONS
• Agriculture
• Forestry
• Inland water
• Geology

Prerequisites

Basic knowledge on physics, computer programming, mathematical and statistical analysis, usually acquired from Bachelor-level courses.

Teaching form

Frontal Lectures in English with slides in power point (Instructional teaching, 4CFU)
Computing Laboratory (Interactive teaching, 2 CFU)
Although not strictly required, attendance to the lectures and practical sessions is strongly recommended. Lectures will be generally held in presence, unless further COVID-19 related restrictions are imposed.

Textbook and teaching resource

• Shunlin Liang, Xiaowen Li and Jindi Wang (2012) Advanced Remote Sensing: Terrestrial Information Extraction and Applications. [S.l.]: Academic Press.
• An Introduction to Statistical Learning. Robert Tibshirani
• Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly
• Slides, scientific manuscripts and handouts are available on the course website

Semester

First

Assessment method

ORAL EXAM AND PROJECT WORK
The student develops a practical project based on the course topics on an environmental application. The oral examination consists in a discussion of the project and an assessment of the theoretical foundations knowledge.

The final grade will be determined by:
-the evaluation of the discussion of the project work
-the evaluation of the knowledge of the different topics covered during the frontal lessons and the laboratory

Office hours

Via appointment by email.

Sustainable Development Goals

QUALITY EDUCATION | GENDER EQUALITY | DECENT WORK AND ECONOMIC GROWTH | INDUSTRY, INNOVATION AND INFRASTRUCTURE
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Key information

Field of research
FIS/07
ECTS
6
Term
First semester
Activity type
Mandatory to be chosen
Course Length (Hours)
56
Degree Course Type
2-year Master Degreee
Language
English

Staff

    Teacher

  • SC
    Sergio Cogliati
  • LS
    Laura Sironi

Students' opinion

View previous A.Y. opinion

Bibliography

Find the books for this course in the Library

Enrolment methods

Manual enrolments
Self enrolment (Student)

Sustainable Development Goals

QUALITY EDUCATION - Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
QUALITY EDUCATION
GENDER EQUALITY - Achieve gender equality and empower all women and girls
GENDER EQUALITY
DECENT WORK AND ECONOMIC GROWTH - Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all
DECENT WORK AND ECONOMIC GROWTH
INDUSTRY, INNOVATION AND INFRASTRUCTURE - Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation
INDUSTRY, INNOVATION AND INFRASTRUCTURE

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