Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2025 352012 Space Science & Technology Applications 1 3.0 3 ★ Ku, Chiung-Shen 太空所 0 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	352012	Space Science & Technology Applications	1	3.0	3	★	Ku, Chiung-Shen	太空所	0	0

Course Plan：

Instructor Ku, Chiung-Shen　Office Hours

Email johnsonku@ntut.edu.tw

Last Updated 2025-05-28 14:29:43

Course Objective 太空科技應用課程旨在介紹太空技術在各種應用領域的應用，並探討太空科技如何影響人類生活和科學研究。課程涵蓋的主題包括：太空探索，地球觀測，通訊和衛星導航，太空工程和材料科學，太空生物學和醫學，太空經濟和商業。通過這些主題的介紹，學生將了解太空科技在各個領域的應用和未來發展方向，並能夠掌握相關技術和知識。 The Space Technology Application Course aims to introduce the application of space technology in various fields, and explore how space technology impacts human life and scientific research. The course covers topics including space exploration, Earth observation, communication and satellite navigation, space engineering and materials science, space biology and medicine, and space economy and business. Through the introduction of these topics, students will understand the application of space technology in various fields and future development directions, and be able to master relevant technologies and knowledge.

Course Schedule 1 Introduction to Space Technology & Remote Sensing 2 Optical Imaging Systems 3 Synthetic Aperture Radar (SAR) Basics 4 SAR Image Interpretation 5 Preprocessing Techniques 6 Space Image Datasets & Platforms 7 Feature Extraction & Classical Method 8 Mini Project 1: SAR vs Optical Comparison 9 Review and Transition to Deep Learning 10 Convolutional Neural Networks (CNNs) 11 Autoencoders & Dimensionality Reduction 12 Anomaly Detection in Space Imagery 13 Deep Reinforcement Learning (DRL) 14 Attention Mechanisms 15 Transformers & Vision Transformers (ViTs) 16 Mini Project 2: Model Implementation 17 Final Report and Presentation 18 Final Report and Presentation

Evaluation and grading policy Interim report 30% Final report 30% Assignment 40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
"Satellite Technology: Principles and Applications" by Anil K. Maini and Varsha Agrawal "Neural Networks and Deep Learning: A Textbook" by Charu C. Aggarwal

The access to curricular consultation Office hour or making an appointment by email

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate AI AI1：使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）

Note
　

Instructor	Ku, Chiung-Shen　Office Hours
Email	johnsonku@ntut.edu.tw
Last Updated	2025-05-28 14:29:43
Course Objective	太空科技應用課程旨在介紹太空技術在各種應用領域的應用，並探討太空科技如何影響人類生活和科學研究。課程涵蓋的主題包括：太空探索，地球觀測，通訊和衛星導航，太空工程和材料科學，太空生物學和醫學，太空經濟和商業。通過這些主題的介紹，學生將了解太空科技在各個領域的應用和未來發展方向，並能夠掌握相關技術和知識。 The Space Technology Application Course aims to introduce the application of space technology in various fields, and explore how space technology impacts human life and scientific research. The course covers topics including space exploration, Earth observation, communication and satellite navigation, space engineering and materials science, space biology and medicine, and space economy and business. Through the introduction of these topics, students will understand the application of space technology in various fields and future development directions, and be able to master relevant technologies and knowledge.
Course Schedule	1 Introduction to Space Technology & Remote Sensing 2 Optical Imaging Systems 3 Synthetic Aperture Radar (SAR) Basics 4 SAR Image Interpretation 5 Preprocessing Techniques 6 Space Image Datasets & Platforms 7 Feature Extraction & Classical Method 8 Mini Project 1: SAR vs Optical Comparison 9 Review and Transition to Deep Learning 10 Convolutional Neural Networks (CNNs) 11 Autoencoders & Dimensionality Reduction 12 Anomaly Detection in Space Imagery 13 Deep Reinforcement Learning (DRL) 14 Attention Mechanisms 15 Transformers & Vision Transformers (ViTs) 16 Mini Project 2: Model Implementation 17 Final Report and Presentation 18 Final Report and Presentation
Evaluation and grading policy	Interim report 30% Final report 30% Assignment 40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes "Satellite Technology: Principles and Applications" by Anil K. Maini and Varsha Agrawal "Neural Networks and Deep Learning: A Textbook" by Charu C. Aggarwal
The access to curricular consultation	Office hour or making an appointment by email
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	AI1：使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
Note