UAT 315 · 3(3-0-6) · Year/term 1/2

Artificial Intelligence, Data Analytics and Computer Vision for Unmanned Aircraft Systems

ปัญญาประดิษฐ์ การวิเคราะห์ข้อมูล และการมองเห็นด้วยคอมพิวเตอร์สำหรับระบบอากาศยานไร้คนขับ

Progress

Notional hours: 135 h (online/self-study 90 · in class/lab 45)

Course description

UAS data pipelines, data preparation and analytics, machine learning, deep learning, object detection, classification, segmentation, and tracking, analysis of multisensor data, model evaluation, real-time processing, and reliability and ethical considerations for AI

Thai description

กระบวนการข้อมูลสำหรับระบบอากาศยานไร้คนขับ การเตรียมและวิเคราะห์ข้อมูล การเรียนรู้ของเครื่อง การเรียนรู้เชิงลึก การตรวจจับ จำแนก แบ่งส่วน และติดตามวัตถุ การวิเคราะห์ข้อมูลจากเซนเซอร์หลายชนิด การประเมินแบบจำลอง การประมวลผลแบบเวลาจริง และข้อพิจารณาด้านความน่าเชื่อถือและจริยธรรมของปัญญาประดิษฐ์

Description source: Curriculum draft (revised 28 Sep 2026)Previous site code: DRT 345

Course learning outcomes (CLO)

CLO	Outcome	PLO
CLO1	Explain AI, data analytics and computer vision	PLO4
CLO2	Design drone data-processing pipelines	PLO4
CLO3	Evaluate AI results with evidence and ethics	PLO4PLO6

Learning modules

1Data and statistics
Weeks 1–3 · 27 hLesson 90 minquiz3 KU

Online (before class) · 18 h

Study the assigned knowledge units in advance, review media and take the module quiz

Descriptive statistics and probability distributionsIn development
Data handling and visualisation with PythonIn development
Limitations of RGB and thermal imageryTH

In class / field · 9 h

Lecture, case discussion and in-class problem solving

Learning evidence: Quiz results and submitted exercises

Open module + quiz

2ML fundamentals
Weeks 4–6 · 27 hLesson 90 minquiz1 KU

Online (before class) · 18 h

Study the assigned knowledge units in advance, review media and take the module quiz

Machine learning fundamentalsIn development

In class / field · 9 h

Lecture, case discussion and in-class problem solving

Learning evidence: Quiz results and submitted exercises

Open module + quiz

3Computer vision
Weeks 7–9 · 27 hLesson 90 minquiz4 KU

Online (before class) · 18 h

Study the assigned knowledge units in advance, review media and take the module quiz

Computer vision and the path to edge AITH
Deep learning and CNNsIn development
GeoAI and publishing mapsTH
AI for screening imagery and damageTH

In class / field · 9 h

Lecture, case discussion and in-class problem solving

Learning evidence: Quiz results and submitted exercises

Open module + quiz

4Datasets and evaluation
Weeks 10–12 · 27 hLesson 90 minquiz4 KU

Online (before class) · 18 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 9 h

Lecture, case discussion and in-class problem solving

Learning evidence: Quiz results and submitted exercises

Open module + quiz

5AI in practice and ethics
Weeks 13–15 · 27 hLesson 90 minquiz3 KU

Online (before class) · 18 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 9 h

Lecture, case discussion and in-class problem solving

Learning evidence: Quiz results and submitted exercises

Open module + quiz

Assessment (draft)

Quizzes and exercises	20%
Midterm examination	30%
Final examination	40%
Participation and discussion	10%

Knowledge domain

D01 Mathematics, physics and statistics D02 Programming and digital technology D09 Law, safety and risk D11 Artificial intelligence and computer vision D13 Surveying, mapping and geoinformatics D14 Inspection, industry and surveillance D16 Public safety and disasters D18 Management, innovation and professional practice

Key references

Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O'Reilly.
Szeliski, R. (2022). Computer vision: Algorithms and applications (2nd ed.). Springer. link
UNESCO. (2021). Recommendation on the ethics of artificial intelligence. link