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| JDZL7420 | 3D Point Cloud Acquisition and Anal. | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of GEOMATICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Hayrettin ACAR | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | Objectives of this course are to learn acquisition techniques and analysis of 3D point cloud data and learning what kind of point cloud data type should be selected in which application. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Have knowledge about 3D Point cloud data formats. | 1 | 1,3, | | PO - 4 : | Know the advantages and disadvantages of different 3D cloud techniques. Evaluate different types of 3D point clouds together. | 4 | 1,3 | | PO - 5 : | Extract the terrain, produce DEM production and simple classifications. | 5 | | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Overview of 3D Point clouds produced by photogrammetric and LiDAR techniques, 3D point cloud production techniques, Passive and active techniques, laser scanners and cameras, Point cloud data pre-processing, ground extraction from 3D point clouds, Vegetation extraction from 3D point clouds, Generation of digital elevation model (DEM) from 3D point clouds, reduction of number of points in 3D point cloud data, Segmentation of 3D point cloud data, Building extraction, Accuracy analysis. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Overview of 3D Point clouds produced by photogrammetric and LiDAR techniques | | | Week 2 | 3D point cloud production techniques | | | Week 3 | Passive and active techniques | | | Week 4 | Laser scanners and cameras | | | Week 5 | Point cloud data pre-processing | | | Week 6 | Ground extraction from 3D point clouds | | | Week 7 | Vegetation extraction from 3D point clouds | | | Week 8 | Mid-term exam | | | Week 9 | Generation of digital elevation model (DEM) from 3D point clouds | | | Week 10 | Reduction of number of points in 3D point cloud data | | | Week 11 | Segmentation of 3D point cloud data | | | Week 12 | Building extraction | | | Week 13 | Accuracy analysis | | | Week 14 | Applications | | | Week 15 | Applications | | | Week 16 | End-of-term exam | | | |
| 1 | Reconstruction and Analysis of 3D Scenes From Irregularly Distributed 3D Points to Object Classes, 2015 | | | |
| 1 | Kraus, K., 2007. Fotogrametri, Fotoğraflardan ve Lazer Tarama Verilerinden Geometrik Bilgiler, Çeviri, İTÜ. | | | 2 | Topographic laser ranging and scanning: principles and processing, Shan, Jie, and Charles K. Toth, 2008. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 18.11.2024 | 2 | 40 | | End-of-term exam | 16 | 27.01.2025 | 2 | 60 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 10 | 14 | 140 | | Arasınav için hazırlık | 10 | 1 | 10 | | Arasınav | 2 | 1 | 2 | | Dönem sonu sınavı için hazırlık | 12 | 1 | 12 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 208 |
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