Digital analysis of remote sensing data–machine learning
| Title | Ψηφιακή ανάλυση δεδομένων τηλεπισκόπησης-μηχανική μάθηση / Digital analysis of remote sensing data–machine learning |
| Code | 08EB056 |
| Faculty | Engineering |
| School | Rural and Surveying Engineering |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Georgios Mallinis |
| Common | No |
| Status | Active |
| Course ID | 600024646 |
Programme of Study: PPS Tmīmatos Agronómōn kai Topográfōn Mīchanikṓn (2025-sīmera)
Registered students: 8
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Geospatial Surveying | EPILOGĪS ALLĪS EMFASĪS | 8 | 4 | 5 |
| Earth Observation using Geodetic Methods | YPOCΗREŌTIKO EPILOGĪS EMFASĪS | 8 | 4 | 5 |
| Construction Surveying | EPILOGĪS ALLĪS EMFASĪS | 8 | 4 | 5 |
| Cadastre and Land Management | YPOCΗREŌTIKO EPILOGĪS EMFASĪS | 8 | 4 | 5 |
| Photogrammetry and Remote Sensing | YPOCΗREŌTIKO EPILOGĪS EMFASĪS | 8 | 4 | 5 |
| Cartography and Geographical Analysis | YPOCΗREŌTIKO EPILOGĪS EMFASĪS | 8 | 4 | 5 |
| Planning and Management of Transportation Infrastructure and Systems | EPILOGĪS ALLĪS EMFASĪS | 8 | 4 | 5 |
| Water Resources, Environment and Engineering Works | EPILOGĪS ALLĪS EMFASĪS | 8 | 4 | 5 |
| Academic Year | 2025 – 2026 |
| Class Period | Spring |
| Faculty Instructors | |
| Weekly Hours | 3 |
| Class ID | 600255089
|
Course Type 2021
Specialization / Direction
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600255089
- eLearning: https://elearning.auth.gr/course/view.php?id=6040
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Prerequisites
General Prerequisites
Basic knowledge on Remote Sensing και προγραμματισμού
Learning Outcomes
Acquisition of theoretical knowledge on related issues. Learning and use of methods and specialized software for Digital Image Processing/Analysis sensing images. Remote sensing image processing through open source software. Through the theoretical lectures will gain a comprehensive and concentrated knowledge of the machine learning methods available for remote sensing data processing. Through practical workshops they will be trained in the appropriate software (open source and commercial) for extracting information with these methods
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Work in an international context
- Design and manage projects
- Respect natural environment
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Digital Remote Sensing images. Practical exercise with application of digital remote sensing methodologies in a study area. Rectification of remote sensing images of high spatial resolution. Integrating multi-temporal images of different spatial analysis and from different sensors. Image radiometric enhancement and geometric correction. Radiometric and spatial enhancement. Principal component analysis, image algebra, indices, classification methods and accuracy assessment.
Machine Learning Techniques: supervised, unsupervised, reinforcement learning
Shallow machine learning: Support Vector Machines, Random Forests, ensemble methods
Deep Learning: Convolutional Neural Networks for classification of remote sensing multi-spectral images, Recurrent Neural Networks for time series analysis
Educational Material Types
- Notes
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Description
The teaching of the course is performed through lectures and slide presentetions using PC and projector in the classroom.
Students receive in electronic form their projects' subject and the material for the implementation of the laboratory exercise. In the PC labs there are installed licenses of specialized digital image processing software for the preparation of the students' project.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | |||
| Laboratory Work | 26 | ✓ | ✓ | ||
| Interactive Teaching in Information Center | 13 | ✓ | |||
| Total | 78 |
Student Assessment
Description
Oral examination and presentation of students using special software and PPT presentation.
Student Assessment methods
- Written Exam with Short Answer Questions (Summative)
- Oral Exams (Summative)
- Labortatory Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Τηλεπισκόπηση (ΣΚΙΑΝΗΣ, ΝΙΚΟΛΑΚΟΠΟΥΛΟΣ, ΒΑΙΟΠΟΥΛΟΣ)
Additional bibliography for study
Σημειώσεις σε ηλεκτρονική μορφή, βοηθήματα λογισμικού για την ψηφιακή επεξεργασία τηλεπισκοπικών εικόνων, Διαφάνειας διαλέξεων σε ψηφιακή μορφή
Last Update
20-02-2025
