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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS (PhD) | |
| Code | UA608 |
| Name | Big Data in Remote Sensing-II |
| Term | 2025-2026 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Belirsiz |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. NAZIM AKSAKER |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to introduce advanced analysis techniques, machine learning, and deep learning approaches in big data applications in remote sensing, and to equip students with the ability to apply these methods on cloud-based platforms.
Course Content
Big data processing methods in remote sensing; machine learning algorithms; deep learning models; advanced analysis applications in Google Earth Engine; classification and regression models; integration of multiple data sources; and project development in cloud-based analysis.
Course Precondition
There are no prerequisites.
Resources
Ghorbanian, A., et al. (2021). Cloud Computing in Remote Sensing: Big Data Applications. Lary, D. J. et al. (2016). Machine Learning in Geosciences. Google Earth Engine Developer Guide. Relevant journal articles and online tutorials.
Notes
Ghorbanian, A., et al. (2021). Cloud Computing in Remote Sensing: Big Data Applications. Lary, D. J. et al. (2016). Machine Learning in Geosciences. Google Earth Engine Developer Guide. Relevant journal articles and online tutorials.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Define advanced analysis methods in remote sensing. |
| LO02 | Describe machine learning and deep learning algorithms. |
| LO03 | Apply advanced analysis in Google Earth Engine. |
| LO04 | Conduct multi-source data integration and project development. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | At the end of the programme, the students acquire advanced knowledge on remote sensing and GIS theory. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | The students gain knowledge on remote sensing technologies, sensors and platforms and remotely sensed data. | 2 |
| PLO03 | Bilgi - Kuramsal, Olgusal | The students generate information using remotely sensed data and GIS together with database management skills. | |
| PLO04 | Bilgi - Kuramsal, Olgusal | The students develop the necessary skills for selecting and using appropriate techniques and tools for engineering practices, using information technologies effectively, and collecting, analysing and interpreting data. | |
| PLO05 | Bilgi - Kuramsal, Olgusal | The students gain knowledge to use current data and methods for multi-disciplinary research. | 3 |
| PLO06 | Bilgi - Kuramsal, Olgusal | The students gain technical competence and skills in using recent GIS and remote sensing software. | |
| PLO07 | Bilgi - Kuramsal, Olgusal | The students acquire knowledge on potential practical fields of use of remotely sensed data, and use their theoretical and practical knowledge for problem solution in the related professional disciplines. | 2 |
| PLO08 | Yetkinlikler - Öğrenme Yetkinliği | Students will be able to calculate and interpret physical and atmospheric variables by processing the satellite data. | 3 |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Students can generate data for GIS projects using Remote Sensing techniques. | 2 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Gains the ability to analyze and interpret geographic data with GIS techniques. | |
| PLO11 | Bilgi - Kuramsal, Olgusal | Gains the ability of problem solving, solving, solution oriented application development. | |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | Acquires the ability to acquire, evaluate, record and apply information from satellite data. | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Advanced big data concepts in remote sensing | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Introduction to machine learning | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Classification algorithms (RF, SVM) | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 4 | Regression algorithms and applications | No preliminary preparation required | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 5 | Machine learning applications in Google Earth Engine | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 6 | Introduction to deep learning | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Tartışma, Soru-Cevap |
| 7 | CNN and its applications in remote sensing | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Tartışma, Soru-Cevap |
| 8 | Mid-Term Exam | Preliminary preparation required | Ölçme Yöntemleri: Yazılı Sınav, Ödev, Proje / Tasarım |
| 9 | Multi-source data integration | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Data integration in GEE and sample applications | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Time series analysis and change detection | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Tartışma |
| 12 | Big data applications in disaster management | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 13 | Project development for cloud-based data | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Tartışma |
| 14 | Project presentations and discussions | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 15 | General evaluation and closing | No preliminary preparation required | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 16 | Term Exams | Preliminary preparation required | Ölçme Yöntemleri: Ödev, Yazılı Sınav, Proje / Tasarım |
| 17 | Term Exams | No preliminary preparation required | Ölçme Yöntemleri: Yazılı Sınav, Ödev, Proje / Tasarım |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 15 | 3 | 45 |
| Out of Class Study (Preliminary Work, Practice) | 15 | 4 | 60 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 20 | 20 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 10 | 10 |
| Final Exam | 1 | 15 | 15 |
| Total Workload (Hour) | 150 | ||
| Total Workload / 25 (h) | 6,00 | ||
| ECTS | 6 ECTS | ||