Piattaforma Kiro - Didattica Curriculare 3+2 e Lauree Magistrali Ciclo Unico

This course aims at providing basic knowledge of data produced by remote sensing, and of the information that can be extracted from them. Radar remote sensing is emphasized, although not treated exclusively, and industry-related applications are stressed. At the end of the course, the student will be able to understand the contents of radar datasets, and to formulate possible uses in real-world applications.

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Please note that the exam is oral, and the schedule published on the School web site reports the formal dates used as a basis for recording marks. The actual exam dates are agreed with each individual student requesting to take the exam. Please do not assume that an exam will take place on the formal date, but rather contact the lecturer for an exam appointment when ready. Available dates are usually published beforehand through the "Announcements" tool on eLearning; you may contact the lecturer proposing one date among these. Please note that the exam can not be sat more than twice in each session (once in the spring session).

This course intends to teach the students a range of processing and analysis techniques commonly applied to spaceborne (i.e., satellite-generated) remotely sensed data, in combination with general geospatial data, in various contexts. The students will learn:

• how remotely sensed data are generated, and what is their physical meaning;
• how remotely sensed and geospatial data can be handled and processed to obtain information about the observed objects and phenomena;
• techniques and methods for leveraging remotely sensed data to generate geospatial information

The course starts from the basics of remote sensing, including its physical interpretation. It then presents and discusses the types of data and their features, to follow with various approaches to data processing and information extraction. Examples of satellite systems will be illustrated. Specific attention will be devoted to emerging technologies like cloud infrastructure and machine/deep learning.

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Please note that the exam is oral, and the schedule published on the School web site reports the formal dates used as a basis for recording marks. The actual exam dates are agreed with each individual student requesting to take the exam. Please do not assume that an exam will take place on the formal date, but rather contact the lecturer for an exam appointment when ready.

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