GMT633 - LASER SCANNING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| LASER SCANNING | GMT633 | Any Semester/Year | 2 | 2 | 3 | 7 |
| Prequisites | None | |||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer Drill and Practice | |||||
| Instructor (s) | Assoc. Prof. Dr. Berkay BAHADUR | |||||
| Course objective | Define basic concepts and usage area of laser scanning technology. | |||||
| Learning outcomes |
| |||||
| Course Content | The basic principles of terrestrial laser scanning (TLS). Workflows and practices for the acquisition and processing of TLS data. An overview of various TLS platforms. Examples of science and education applications such as outcrop mapping, deformation monitoring and data processing. Fundamentals of LIDAR systems. The basic engineering trade-offs in the transmitter, receiver, and data acquisition subsystems. Calculation of signal-to-noise ratios for typical measurements. Advanced LIDAR techniques to characterize atmospheric constituents and parameters. | |||||
| References | - Shan, J., Toth, C.K., 2018. Topographic Laser Ranging and Scanning: Principles and Processing (2nd edition). CRC Press. - Vosselman, G., Maas, H., 2010. Airborne and Terrestrial Laser Scanning. CRC Press. - Renslow, M.S., 2012. Manual of Airborne Topographic Lidar. American Society for Photogrammetry and Remote Sensing. - Li, Z., Zhu, Q., Gold, C., 2005. Digital Terrain Modeling: Principles and Methodology. CRC Press. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | The basic principles of terrestrial laser scanning (TLS) |
| Week 2 | The basic principles of terrestrial laser scanning (TLS) |
| Week 3 | Workflows and practices for the acquisition and processing of TLS data. |
| Week 4 | Workflows and practices for the acquisition and processing of TLS data. |
| Week 5 | An overview of various TLS platforms |
| Week 6 | Midterm exam |
| Week 7 | Examples of science and education applications, such as outcrop mapping, deformation monitoring and data processing |
| Week 8 | Examples of science and education applications, such as outcrop mapping, deformation monitoring and data processing |
| Week 9 | Fundamentals of LIDAR systems |
| Week 10 | Fundamentals of LIDAR systems |
| Week 11 | Midterm exam |
| Week 12 | The basic engineering trade-offs in the transmitter, receiver, and data acquisition subsystems |
| Week 13 | Advanced LIDAR techniques used to characterize atmospheric constituents and parameters. |
| Week 14 | Advanced LIDAR techniques used to characterize atmospheric constituents and parameters. |
| Week 15 | Final preparation |
| Week 16 | Final exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 16 | 5 |
| Laboratory | 14 | 10 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 5 | 5 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 2 | 30 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 37 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 16 | 2 | 32 |
| Laboratory | 0 | 0 | 0 |
| Application | 14 | 2 | 28 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 5 | 70 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 5 | 8 | 40 |
| Midterms (Study duration) | 2 | 13 | 26 |
| Final Exam (Study duration) | 1 | 14 | 14 |
| Total Workload | 52 | 44 | 210 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Define problems in Geomatics Engineering and use Information Technology effectively in order to solve these problems. | X | ||||
| 2. Learn basic Mathematics, Science and Engineering formations and use them productively in professional life | X | ||||
| 3. Choose, use and improve recent technology and methods that needed for Geomatics Engineering applications | X | ||||
| 4. Earn the ability of producing new spatial products with data coming from international Geomatics application by using his/her qualification of obtaining, interpretation and analyzing of spatial data and by adding personal viewpoint | X | ||||
| 5. Estimate geodetic and geodynamic parameters with geodetic observations and use kinematic and dynamic functional models effectively in studies | X | ||||
| 6. Know advanced national and international applications in areas of Photogrammetry and Laser Scanning and contribute to the development processes of these applications | X | ||||
| 7. Develop strategies for data collection from space/aerial images and aerial/terrestrial laser scanning data; define the most appropriate methods for data extraction from collected data; process, analysis, integrate data with other spatial data, develop models; attend to field works and present results and outputs visually, statistically and thematically | X | ||||
| 8. Develop case / aim specific static or dynamic online systems, design spatial database management systems and produce visual products by following recent developments in GIS environment | X | ||||
| 9. Find solutions for aim relevant data obtainment by being familiar with working principle of scanning devices and sensors and their usage areas | X | ||||
| 10. Design systems which are considering scientific facts for more economically and more reliable management of industrial and infrastructure applications | X | ||||
| 11. Consider factors of social, environmental, economic, health and job security in professional life. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest