Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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  • Türkçe
About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Institute of Graduate Programmes
  • Remote Sensing and Geographical Information Syst.
  • Master of Science (MS) Degree
  • Course Structure Diagram with Credits
  • Raster and Grid Modeling on Remote Sensing
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information

Code - Course Title UCS542 - Raster and Grid Modeling on Remote Sensing
Course Type Elective Courses
Language of Instruction Türkçe
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ METİN ALTAN
Mode of Delivery This course is given only face to face
Prerequisites There is no prerequisite or co-requisite for this course.
Courses Recomended There is no recommended course.
Required or Recommended Resources There are no recommended resources.
Recommended Reading List There is no recommended reading list.
Assessment methods and criteria Midterm and Final exam are held. Final ` Written Exam
Work Placement Internship and application is not obligatory.
Sustainability Development Goals

Content

Weeks Topics
Week - 1 Presenting properties of Raster data.
Week - 2 Presenting properties of Grid data
Week - 3 Presenting data sources and techniques of data production
Week - 4 Presenting applications of Raster and Grid data production
Week - 5 Presenting point, line and polygon projection on data formats
Week - 6 Presenting positional sensibility and resolution
Week - 7 Presenting change detection analysis on the same coordinate
Week - 8 Presenting production techniques of Digital Elevation Model
Week - 9 Presenting applications based on Digital Elevation Model
Week - 10 Presenting algebraic functions which are used on data modelling.
Week - 11 Presenting recode applications on data analysis
Week - 12 Presenting grading applications on data analysis
Week - 13 Presenting modelling techniques with multi fuctor contributed
Week - 14 Presenting modelling applications with multi fuctor contributed

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Observation
  • Field Trip
  • Experiment
  • Drill - Practise
  • Case Study
  • Problem Solving
  • Brain Storming
  • Report Preparation and/or Presentation
  • Competences
  • Productive
  • True to core values
  • Creative
  • Civic awareness
  • Effective use of Turkish
  • Environmental awareness
  • Effective use of a foreign language
  • Adapt to different situations and social roles

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
2.Midterm Exam 1 40
Final Exam 1 60
Toplam (%) 100
  • Info on the Institution
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