• Training Structure

    College of Sciences

  • Time of year

    Fall

Description

This module aims to provide a foundation in the principles of positioning and topographic mapping. Basic knowledge of GNSS and laser positioning methods is covered in class and then applied in the field and during practical exercises. Finally, a project assignment is designed to enable students to apply the practical and theoretical knowledge covered at the beginning of the module and, above all, to better understand the complementary nature and accuracy of geodetic measurements.

Course Content:
- Introduction to Terrestrial and Space Geodesy
- Reference Frames in Geodesy
- Traditional Tools of Terrestrial Geodesy
- The GNSS Positioning System
- Applications of Geodesy (active tectonics, landslides, anthropogenic deformation, etc.)
- Topographic surveying (DTM, LIDAR, etc.)

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Objectives

- Theoretical approach: physical principles of measurements and errors (lectures)
- Fieldwork on a case study: multi-instrumental (leveling with a theodolite and geodimeter, GNSS, LIDAR, DTM)
- Project work: data integration and interpretation (lab / tutorial / independent work)

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Class Hours

  • Positioning & Remote Sensing - TutorialTutorials12 hours
  • Positioning & Remote Sensing - CMLecture12:00 p.m.
  • Positioning & Remote Sensing - LabLab Work12 hours

Mandatory Prerequisites

Required prerequisites*: None

Recommended prerequisites*: 

- Basic statistics (mean, standard deviation, histogram, and a few distributions)

- Python Basics (loops, variables, arrays, reading ASCII files)

- Geographic projection and coordinate system

- Altitude (geoid) and height (ellipsoid)

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Knowledge Assessment

100% Continuous assessment in the form of multiple-choice questions, lab reports, and field reports

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Target Skills

Skills required for course completion:
- GNSS positioning systems
- Topographic surveys using GNSS, laser, and leveling (practical)
- DTM, DEM (resolution, interpolation)

Additional skills:
- Organizing a field experiment
- Quantifying data uncertainties based on the objective
- Processing a heterogeneous dataset

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