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 develop a solid understanding of estimation theory and its applications in geospatial disciplines, especially in the domain of topography.
Through a hands-on approach using Python, students will learn to formulate and solve estimation problems for linear and nonlinear models, with particular emphasis on observation errors, and the application of least-squares estimation, and the best linear unbiased estimator (BLUE). Students will also learn to assess the quality and reliability of the estimated results, including their precision and the statistical testing of the adopted models.
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 and estimation theory;
- formulate and solve linear and nonlinear estimation problems using least-squares and BLUE;
- assess the precision and reliability of estimated results and test the adopted models;
- use Python to apply estimation theory to practical surveying problems.
Prerequisite knowledge and skills
- Basic calculus, including partial derivatives (needed for linearisation); trigonometry; basic probability and statistics (mean, variance, the normal distribution); basic linear algebra (matrix and vector operations, the transpose and the inverse, systems of linear equations, the rank of a matrix)
- 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.
Submission of all scripts implemented during the exercise sessions is a mandatory prerequisite for admission to the written exam.
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:
- eCampus
- Microsoft Teams
Further information:
Course slides will be available on Teams platform 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