UA0015

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code UA0015
Name
Term 2024-2025 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 no prerequisites for the course Öğretim Yöntemleri:
Anlatım
2 Literature search for Big Data in Remote Sensing2 no prerequisites for the course Öğretim Yöntemleri:
Anlatım
3 Literature search for Big Data in Remote Sensing3 no prerequisites for the course Öğretim Yöntemleri:
Anlatım
4 Subscribing to the free database no prerequisites for the course Öğretim Yöntemleri:
Anlatım
5 Selecting the remote sensing platform and downloading data no prerequisites for the course Öğretim Yöntemleri:
Anlatım
6 Application of temporal analyzes to data no prerequisites for the course Öğretim Yöntemleri:
Anlatım
7 Making the coding no prerequisites for the course Öğretim Yöntemleri:
Anlatım
8 Mid-Term Exam midterm Ölçme Yöntemleri:
Ödev, Yazılı Sınav
9 Evaluation of outputs no prerequisites for the course Öğretim Yöntemleri:
Anlatım
10 Creation of tables and graphs no prerequisites for the course Öğretim Yöntemleri:
Anlatım
11 Reporting of the study no prerequisites for the course Öğretim Yöntemleri:
Anlatım
12 Reporting of the study(Cont) no prerequisites for the course Öğretim Yöntemleri:
Anlatım
13 Evaluating reports and converting them into articles no prerequisites for the course Öğretim Yöntemleri:
Anlatım
14 Writing and presenting projects to different platforms no prerequisites for the course Öğretim Yöntemleri:
Anlatım
15 Writing and presenting projects to different platforms cont no prerequisites for the course 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

Update Time: 23.09.2024 03:05