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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS (PhD) | |
| Code | UA607 |
| Name | Big Data in Remote Sensing-I |
| Term | 2025-2026 Academic Year |
| Term | Fall |
| 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 teach fundamental concepts and methods for managing, processing, and analyzing the increasing volume of data in remote sensing. Students will gain skills in basic data processing and analysis within big data environments and learn to use cloud-based platforms effectively.
Course Content
Concept of big data in remote sensing; data sources (Sentinel, MODIS, Landsat, etc.); cloud-based data processing (Google Earth Engine, AWS); basic preprocessing methods; data management; automated workflows; and introduction to multi-temporal data analysis.
Course Precondition
No prerequisites.
Resources
Ghorbanian, A., et al. (2021). Cloud Computing in Remote Sensing: Big Data Applications. Google Earth Engine Developer Guide. Sentinel, Landsat ve MODIS Kullanıcı Kılavuzları / Sentinel, Landsat, and MODIS User Guides.
Notes
Ghorbanian, A., et al. (2021). Cloud Computing in Remote Sensing: Big Data Applications. Google Earth Engine Developer Guide. Sentinel, Landsat ve MODIS Kullanıcı Kılavuzları / Sentinel, Landsat, and MODIS User Guides.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Define the concept of big data in remote sensing. |
| LO02 | Describe big data sources and cloud platforms. |
| LO03 | Perform basic data processing on cloud-based platforms. |
| LO04 | Conduct introductory multi-temporal data analyses. |
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. | 2 |
| 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. | |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Students can generate data for GIS projects using Remote Sensing techniques. | 3 |
| PLO10 | Bilgi - Kuramsal, Olgusal | Gains the ability to analyze and interpret geographic data with GIS techniques. | 2 |
| 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. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to big data in remote sensing | No advance preparation required. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 2 | Big data sources (Sentinel, Landsat, MODIS etc.) | No advance preparation required. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Cloud computing concepts and platforms (GEE, AWS) | No advance preparation required. | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 4 | Google Earth Engine interface and data access | No advance preparation required. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 5 | Basic concepts of image preprocessing | No advance preparation required. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 6 | Data management and dataset organization | No advance preparation required. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 7 | Raster data processing applications | No advance preparation required. | Öğretim Yöntemleri: Anlatım, Tartışma, Soru-Cevap |
| 8 | Mid-Term Exam | Advance preparation required. | Ölçme Yöntemleri: Ödev, Proje / Tasarım |
| 9 | Vector data processing and spatial analysis | No advance preparation required. | Öğretim Yöntemleri: Soru-Cevap, Tartışma |
| 10 | Mapping and visualization in Google Earth Engine | No advance preparation required. | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 11 | Creating automated data workflows | No advance preparation required. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 12 | Introduction to multi-temporal data analysis | No advance preparation required. | Öğretim Yöntemleri: Soru-Cevap, Anlatım, Alıştırma ve Uygulama |
| 13 | Practical multi-temporal analysis | No advance preparation required. | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 14 | Project presentations and discussions | No advance preparation required. | Öğretim Yöntemleri: Soru-Cevap, Anlatım |
| 15 | General evaluation and closing | No advance preparation required. | Öğretim Yöntemleri: Anlatım, Tartışma, Soru-Cevap |
| 16 | Term Exams | Advance preparation required | Ölçme Yöntemleri: Yazılı Sınav, Ödev, Proje / Tasarım |
| 17 | Term Exams | Advance preparation required | Ölçme Yöntemleri: Proje / Tasarım, Sözlü Sınav |
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 | ||