Price : 500 EGP

Machine Learning for Remote Sensing

Course Description:

This course introduces machine learning (ML) techniques for remote sensing. The course will cover the fundamentals of ML, including supervised learning, unsupervised learning, and reinforcement learning. The course will also cover the application of ML 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 machine learning

Apply machine learning to remote sensing problems

Evaluate the performance of machine learning models

Use machine learning to solve real-world problems


Introduction to Remote Sensing

Introduction to Statistics


Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.

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

Course Outline:

Module 1: Introduction to machine learning

Module 2: Supervised learning

Module 3: Unsupervised learning

Module 4: Reinforcement learning

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)


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

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