Training structure
Faculty of Science
Time of year
Autumn
Description
This module aims to provide the basics of positioning and topographic mapping principles. Basic knowledge of GNSS and laser positioning methods is detailed in class and then used in the field and during practical work. Finally, a project assignment allows students to apply the practical and theoretical knowledge acquired at the beginning of the module and, above all, to better understand the complementarity and accuracy of geodetic measurements.
Course content:
- Introduction to ground geodesy and space geodesy
- Reference frames in geodesy
- Traditional ground geodesy tools
- The GNSS positioning system
- Applications of geodesy (active tectonics, landslides, anthropogenic deformation, etc.)
- Topographic measurement (DTM, LIDAR, etc.)
Objectives
- Theoretical approach: physical principles of measurements and errors (lecture)
- Fieldwork on a case study: multi-instrumental (theodolite and geodimeter leveling, GNSS, LIDAR, DTM)
- Project work: data combination and interpretation (practical work/tutorials/individual work)
Teaching hours
- Positioning & Remote Sensing - TutorialTutorials12 p.m.
- Positioning & Remote Sensing - CMLecture12 p.m.
- Positioning & Remote Sensing - Practical WorkPractical Work12 p.m.
Mandatory prerequisites
Required prerequisites*: None
Recommended prerequisites:
- Basic statistics (mean, standard deviation, histogram, and some distributions)
- Basic Python (loops, variables, arrays, reading ASCII files)
- Geographic projection and coordinate system
- Altitude (geoid) and height (ellipsoid)
Knowledge assessment
100% Continuous assessment in the form of multiple-choice questions, practical work reports, field reports
Targeted skills
Skills required for EU validation:
- GNSS positioning system
- GNSS, laser, and leveling topographic measurements (practical)
- DTM, DEM (resolution, interpolation)
Additional skills:
- Organization of a field experiment
- Quantification of data uncertainties based on the objective
- Processing of a heterogeneous dataset