Course Syllabus

Course Information：

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

Fall Semester
Year 2026 366646 Space Science & Technology Applications 1 3.0 3 ★ Trong-An Bui 4EOE4 11 0 光電四、光電所選修和太空所必修合開、億光405教

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	366646	Space Science & Technology Applications	1	3.0	3	★	Trong-An Bui	4EOE4	11	0	光電四、光電所選修和太空所必修合開、億光405教

Course Plan：

Instructor Trong-An Bui　Office Hours

Email trongan93@ntut.edu.tw

Last Updated 2026-07-17 11:40:29

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 Week 1: Introduction to Space Technology and Earth Observation Week 2: Remote-Sensing Data Fundamentals Week 3: Space Image Processing and Data Preparation Week 4: Deep-Learning Foundations for Space Applications Week 5: Convolutional Neural Networks for Earth Observation Week 6: Tiny-Object Detection in Satellite Imagery Week 7: Midterm Examination and Project Proposal Week 8: Semantic Segmentation and Change Detection Week 9: Advanced CNNs and Efficient Onboard Inference Week 10: Vision Transformers and Foundation Models for Earth Observation Week 11: Generative AI for Remote-Sensing Images Week 12: Autoencoders and Anomaly Detection Week 13: Advanced Space-AI Applications Week 14: Deep Reinforcement Learning and Autonomous Space Systems Week 15: Onboard and Agentic AI for Space Missions Week 16: Security, Reliability, and Ethics of Space AI Week 17: Final Project Reports and Presentations I Week 18: Final Project Reports and Presentations II

Evaluation and grading policy 出席、課堂參與及討論 / Attendance, Class Participation, and Discussion: 10% 作業與實作練習 / Assignments and Practical Exercises: 20% 期中考試 / Midterm Examination: 20% 專題提案與進度報告 / Project Proposal and Progress Report: 10% 期末專題報告 / Final Project Report: 25% 期末口頭簡報 / Final Presentation: 15%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
教師自編講義、課堂簡報及線上教學資源 Instructor-prepared lecture notes, presentation slides, and online learning materials. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016. François Chollet, Deep Learning with Python, 2nd Edition, Manning Publications, 2021. Richard Szeliski, Computer Vision: Algorithms and Applications, 2nd Edition, Springer, 2022. John A. Richards, Remote Sensing Digital Image Analysis, 6th Edition, Springer, 2022. Thomas M. Lillesand, Ralph W. Kiefer, and Jonathan W. Chipman, Remote Sensing and Image Interpretation, 7th Edition, Wiley, 2015. NASA、ESA、USGS、TASA及其他太空與遙測機構所提供的開放資料、技術文件與案例研究。 Open datasets, technical documentation, and case studies provided by NASA, ESA, USGS, TASA, and other space and remote-sensing organizations. 課程相關之最新期刊論文、國際研討會論文、開源程式碼及衛星影像資料集。 Selected journal articles, conference papers, open-source software, and satellite-imagery datasets related to the course.

The access to curricular consultation In-class and after-class consultation. Office or MS Teams consultation by appointment.
Email: trongan93@ntut.edu.tw

Expanding teaching and resources ● 混成學習 (Blended Learning)
課程結合實體授課與線上學習（同步視訊／非同步活動）。
Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities).
● 參與式創新教學 (Participatory & Innovative Teaching)
強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。
Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification.

The course corresponds to the SDGs SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Does the course incorporate AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）

Note
　

Instructor	Trong-An Bui　Office Hours
Email	trongan93@ntut.edu.tw
Last Updated	2026-07-17 11:40:29
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	Week 1: Introduction to Space Technology and Earth Observation Week 2: Remote-Sensing Data Fundamentals Week 3: Space Image Processing and Data Preparation Week 4: Deep-Learning Foundations for Space Applications Week 5: Convolutional Neural Networks for Earth Observation Week 6: Tiny-Object Detection in Satellite Imagery Week 7: Midterm Examination and Project Proposal Week 8: Semantic Segmentation and Change Detection Week 9: Advanced CNNs and Efficient Onboard Inference Week 10: Vision Transformers and Foundation Models for Earth Observation Week 11: Generative AI for Remote-Sensing Images Week 12: Autoencoders and Anomaly Detection Week 13: Advanced Space-AI Applications Week 14: Deep Reinforcement Learning and Autonomous Space Systems Week 15: Onboard and Agentic AI for Space Missions Week 16: Security, Reliability, and Ethics of Space AI Week 17: Final Project Reports and Presentations I Week 18: Final Project Reports and Presentations II
Evaluation and grading policy	出席、課堂參與及討論 / Attendance, Class Participation, and Discussion: 10% 作業與實作練習 / Assignments and Practical Exercises: 20% 期中考試 / Midterm Examination: 20% 專題提案與進度報告 / Project Proposal and Progress Report: 10% 期末專題報告 / Final Project Report: 25% 期末口頭簡報 / Final Presentation: 15%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes 教師自編講義、課堂簡報及線上教學資源 Instructor-prepared lecture notes, presentation slides, and online learning materials. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016. François Chollet, Deep Learning with Python, 2nd Edition, Manning Publications, 2021. Richard Szeliski, Computer Vision: Algorithms and Applications, 2nd Edition, Springer, 2022. John A. Richards, Remote Sensing Digital Image Analysis, 6th Edition, Springer, 2022. Thomas M. Lillesand, Ralph W. Kiefer, and Jonathan W. Chipman, Remote Sensing and Image Interpretation, 7th Edition, Wiley, 2015. NASA、ESA、USGS、TASA及其他太空與遙測機構所提供的開放資料、技術文件與案例研究。 Open datasets, technical documentation, and case studies provided by NASA, ESA, USGS, TASA, and other space and remote-sensing organizations. 課程相關之最新期刊論文、國際研討會論文、開源程式碼及衛星影像資料集。 Selected journal articles, conference papers, open-source software, and satellite-imagery datasets related to the course.
The access to curricular consultation	In-class and after-class consultation. Office or MS Teams consultation by appointment. Email: trongan93@ntut.edu.tw
Expanding teaching and resources	● 混成學習 (Blended Learning) 課程結合實體授課與線上學習（同步視訊／非同步活動）。 Combines face-to-face instruction with online learning (including synchronous video sessions or asynchronous activities). ● 參與式創新教學 (Participatory & Innovative Teaching) 強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。 Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification.
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
Note