Duration
20h Th, 30h Pr
Number of credits
| Master in geography: geomatics, professional focus in geodata expert | 5 crédits | |||
| Master in geography: geomatics, professional focus in land surveyor | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
In this course, students will explore the theoretical and practical aspects of observation theory.
They will gain a deep understanding of estimation theory and its practical applications in surveying.
Through a hands-on approach using Python programming, students will learn to solve estimation exercises, with a focus primarily on error theory and the application of the least squares methodology.
The learning process is structured around two complementary approaches:
- theoretical sessions, providing the necessary conceptual foundations;
- exercise sessions in Python, enabling students to directly apply theoretical knowledge.
Learning outcomes of the learning unit
At the end of this course, students will be able to:
- understand the fundamental principles of observation theory;
- apply estimation theory concepts to solve real-world surveying problems;
- use Python efficiently for data analysis and problem-solving;
- implement the theory of errors and least squares methodology in practical scenarios.
Prerequisite knowledge and skills
- Basic knowledge of mathematic calculus, trigonometry, and statistics
- Understanding of basic programming concepts
Planned learning activities and teaching methods
Each theoretical lecture is complemented by a Python exercise session, giving students the opportunity to directly apply the concepts of Observation Theory from different geospatial disciplines, especially in the domain of topography.
For each exercise session, students are required to submit a working Python script.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face learning.
Course materials and recommended or required readings
Platform(s) used for course materials:
- Microsoft Teams
Further information:
Course slides will be available om Teams platforms and regularly updated after each class.
Exam(s) in session
Any session
- In-person
written exam ( open-ended questions ) AND oral exam
Continuous assessment
Further information:
The evaluation consists of:
(1) a written exam (on PC) covering:
- estimation exercises to be solved in Python, similar to those practiced during the exercise sessions of the course;
- open questions on theoretical aspects related to the estimation problems covered in the exercises;
Submission of all scripts implemented during the exercise sessions is a mandatory prerequisite for admission to the written exam.
Work placement(s)
Organisational remarks and main changes to the course
The course is given during the first semester (3 hour lecture per week).
The course will be taught in English.
Contacts
Roberta Ravanelli, Chargée de cours
Geomatics Unit
Quartier Agora
Allée du 6 août (B5a), 19 - 4000 Liège
E-mail: roberta.ravanelli@uliege.be