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Information
| Code | UA0015 |
| Name | |
| Term | 2022-2023 Academic Year |
| Semester | . Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator |
Course Goal / Objective
It is aimed to learn the satellites used in Remote Sensing, different data types and different analysis techniques.
Course Content
Numerous Earth Observation spaceborne and airborne sensors (MODIS, VIIRS, SEVIRI, etc) from many different countries every day provide a large amount of remotely sensed data. These data can be used for natural hazard monitoring, global climate change, urban planning, etc. It is used for different applications. Practices are data-driven and often multidisciplinary. Based on this, we can say that we are currently living in the age of large remote sensing data. Our focus is to analyze what exactly big data means in remote sensing applications and how big data can add value in this context. Moreover, this course covers the most challenging issues in managing, processing and using big data efficiently for remote sensing problems. To illustrate the points mentioned above, two case studies discussing the use of big data in remote sensing are shown. Both cases are also used to illustrate the significant challenges and opportunities brought by the use of big data in remote sensing applications.
Course Precondition
None
Resources
Lecture Notes
Notes
Lecture Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Recognizes Remote Sensing platforms |
| LO02 | Learns to retrieve data from different databases. |
| LO03 | Learn to analyze different data types together |
| LO04 | For big data, IDL uses programming languages such as Python. |
| LO05 | He can turn her work into publication. |
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. | 2 |
| PLO02 | Bilgi - Kuramsal, Olgusal | The students gain knowledge on remote sensing technologies, sensors and platforms and remotely sensed data. | 3 |
| 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. | 3 |
| 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. | 3 |
| 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. | |
| 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. | 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. | 4 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Literature search for Big Data in Remote Sensing | None | Öğretim Yöntemleri: Anlatım |
| 2 | Literature search for Big Data in Remote Sensing2 | None | Öğretim Yöntemleri: Anlatım |
| 3 | Literature search for Big Data in Remote Sensing3 | None | Öğretim Yöntemleri: Anlatım |
| 4 | Subscribing to the free database | None | Öğretim Yöntemleri: Anlatım |
| 5 | Selecting the remote sensing platform and downloading data | None | Öğretim Yöntemleri: Anlatım |
| 6 | Application of temporal analyzes to data | None | Öğretim Yöntemleri: Anlatım |
| 7 | Making the coding | None | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | midterm | Ölçme Yöntemleri: Ödev, Yazılı Sınav |
| 9 | Evaluation of outputs | None | Öğretim Yöntemleri: Anlatım |
| 10 | Creation of tables and graphs | None | Öğretim Yöntemleri: Anlatım |
| 11 | Reporting of the study | None | Öğretim Yöntemleri: Anlatım |
| 12 | Reporting of the study(Cont) | None | Öğretim Yöntemleri: Anlatım |
| 13 | Evaluating reports and converting them into articles | None | Öğretim Yöntemleri: Anlatım |
| 14 | Writing and presenting projects to different platforms | None | Öğretim Yöntemleri: Anlatım |
| 15 | Writing and presenting projects to different platforms cont | None | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | exam | Ölçme Yöntemleri: Yazılı Sınav, Ödev |
| 17 | Term Exams | exam | Ölçme Yöntemleri: Ödev, Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||