Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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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.
  • Remote Sensing and Geographical Information Syst.
  • Course Structure Diagram with Credits
  • Airborne Laser Scanning (LIDAR) Systems
  • Description
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load

Course Introduction Information

Code - Course Title UCS624 - Airborne Laser Scanning (LIDAR) Systems
Course Type Elective Courses
Language of Instruction Türkçe
Laboratory + Practice 3+0
ECTS 7.5
Course Instructor(s) DOKTOR ÖĞRETİM ÜYESİ RESUL ÇÖMERT
Mode of Delivery This course is given face to face.
Prerequisites There is no prerequisite or co-requisite for this course
Courses Recomended For the course, it is expected to have knowledge about image classification in remote sensing.
Required or Recommended Resources There is no recommended reading source.
Recommended Reading List Simonovic, Slobodan P. Role of remote sensing in disaster management. Department of Civil and Environmental Engineering, The University of Western Ontario, 2002.
Assessment methods and criteria 1 midterm exams, 1 final exam and 1 coursework
Work Placement Not Applicable
Sustainability Development Goals Industry, Innovation and Infrastructure , Sustainable Cities and Communities , Terrestrial Life

Content

Weeks Topics
Week - 1 Introduction to the Course and Overview of LiDAR Technology
Week - 2 Airborne LiDAR Systems
Week - 3 Terrestrial LiDAR Systems
Week - 4 Application Areas of Airborne and Terrestrial LiDAR
Week - 5 Point Cloud Data Structure and Attributes
Week - 6 Point Cloud Filtering: Noise Removal and Preprocessing
Week - 7 Ground Filtering Algorithms and DTM Generation
Week - 8 DSM Generation and Surface Models
Week - 9 Point Cloud Classification Methods
Week - 10 Information Extraction from Point Clouds
Week - 11 Lab: Point Cloud Visualization and Filtering
Week - 12 Lab: DTM and DSM Generation
Week - 13 Lab: Point Cloud Classification
Week - 14 Project Presentations and General Evaluation

Learning Activities and Teaching Methods

  • Teaching Methods
  • Lecture
  • Discussion
  • Question & Answer
  • Team/Group Work
  • Demonstration
  • Drill - Practise
  • Case Study
  • Brain Storming
  • Report Preparation and/or Presentation
  • Proje Design/Management
  • Competences
  • Productive
  • True to core values
  • Rational
  • Questoning
  • Entrepreneur
  • Creative
  • Follow ethical and moral rules
  • Civic awareness
  • Environmental awareness
  • Effective use of a foreign language
  • Adapt to different situations and social roles
  • Work in teams
  • Use time effectively
  • Problem solving
  • Applying theoretical knowledge into practice
  • Information Management
  • To work autonomously
  • Elementary computing skills
  • Decision making
  • To work in interdisciplinary projects
  • Leadership

Assessment Methods

Assessment Method and Passing Requirements
Quamtity Percentage (%)
1.Midterm Exam 1 40
Final Exam 1 60
Toplam (%) 100
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