Big Data Analytics for Remote Sensing

2023-10-26

Course Description:

This course introduces big data analytics techniques for remote sensing. The course will cover the fundamentals of big data analytics, including data mining, machine learning, and cloud computing. The course will also cover the application of big data analytics to remote sensing problems, such as land cover classification, object detection, and change detection.

Course Objectives:

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

○ Understand the fundamentals of big data analytics

○ Apply big data analytics to remote sensing problems

○ Evaluate the performance of big data analytics models

○ Use big data analytics to solve real-world problems

Prerequisites:

○ Introduction to Remote Sensing

○ Introduction to Statistics

○ Introduction to Programming

Textbooks:

○ Chen, Ming-Hsiang, and Philip S. Yu. Big Data Analytics: A Practical Guide for Business. Morgan Kaufmann, 2016.

○ Amini, Bahram, and Saeed Amini. Machine Learning for Remote Sensing. CRC Press, 2018.

Course Outline:

○ Module 1: Introduction to big data analytics

○ Module 2: Data mining

○ Module 3: Machine learning

○ Module 4: Cloud computing

○ Module 5: Land cover classification

○ Module 6: Object detection

○ Module 7: Change detection

○ 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.