Cloud Computing for Remote Sensing

2023-10-22

Course Description:

This course introduces cloud computing techniques for remote sensing. The course will cover the fundamentals of cloud computing, including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). The course will also cover the application of cloud computing to remote sensing problems, such as data storage, data processing, and data analysis.

Course Objectives:

Upon completion of this course, students will be able to:

○ Understand the fundamentals of cloud computing

○ Apply cloud computing to remote sensing problems

○ Evaluate the performance of cloud computing platforms

○ Use cloud computing to solve real-world problems

Prerequisites:

○ Introduction to Remote Sensing

○ Introduction to Programming

Textbooks:

○ Buyya, Rajkumar, et al. Cloud Computing: Principles and Paradigms. 3rd ed. Springer, 2014.

○ Amini, Bahram, and Saeed Amini. Cloud Computing for Remote Sensing. CRC Press, 2019.

Course Outline:

○ Module 1: Introduction to cloud computing

○ Module 2: Infrastructure as a service (IaaS)

○ Module 3: Platform as a service (PaaS)

○ Module 4: Software as a service (SaaS)

○ Module 5: Remote sensing data storage

○ Module 6: Remote sensing data processing

○ Module 7: Remote sensing data analysis

○ Module 8: Case studies

Course Delivery Method:

○ Lectures: Interactive lectures delivered by experienced remote sensing professionals and experts through presentations, videos, and demonstrations.

○ Homework Assignments: There will be three homework assignments throughout the course. The homework assignments will be graded on correctness and completeness.

○ Project: The project will involve applying machine learning to a remote sensing problem. Students will work in groups of two or three to complete the project. The project will be graded on creativity, originality, and technical quality.

○ Q&A Sessions: Regular question and answer sessions to clarify doubts and address participants' queries related to the course content and practical exercises.

Course Duration:

4 weeks (can be adjusted as per requirements)

Note:

The course content and duration can be customized according to the specific requirements and level of expertise of the target audience.