Duration
80h Proj.
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
Coordinator
Language(s) of instruction
French language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
As part of this course-project, each student carries out an individual assignment focused on a problem (environmental, societal, etc.) defined in agreement with the instructor. The objective is to apply remote sensing knowledge through a complete scientific approach:
- Definition of a problem and selection of relevant data.
- Data preprocessing (e.g., radiometric and geometric corrections, image preparation).
- Analysis and information extraction: spectral indices, classification, change detection, or advanced methods (e.g., machine learning).
- Thematic application: study of ecosystems, land cover mapping, vegetation monitoring, natural hazard analysis, etc.
- Interpretation and presentation of results in a scientific report and an oral presentation.
Learning outcomes of the learning unit
By the end of this course, students will be able to design and carry out an individual remote sensing project, preprocess and analyze spatial data to address a specific problem, produce clear maps, charts, and reports, and present and defend their results independently and critically.
This course-project aims to foster autonomy, methodological rigor, and critical analysis skills, while also developing scientific communication abilities.
Prerequisite knowledge and skills
Knowledge of the theoretical foundations of remote sensing.
Remote Sensing course GEOG0060 / SPAT0032 or an equivalent course completed at another university.
Planned learning activities and teaching methods
This course focuses on an individual project, allowing each student to develop practical and analytical skills in remote sensing through an independent yet supervised approach.
Students will define their research problem in agreement with the instructor, carry out data preprocessing and analysis, and apply appropriate methods (classification, spectral indices, change detection, etc.). Personalized guidance will be provided by the instructor and teaching assistant to validate the methodological approach. The project will conclude with a written report and an oral presentation to present and defend the results.
Mode of delivery (face to face, distance learning, hybrid learning)
Blended learning
Further information:
Project-based approach
The course combines individualized supervision sessions tailored to the student's pace, independent project work, and an oral presentation accompanying the submission of the report. This approach allows students to progress independently while receiving regular pedagogical feedback.
Course materials and recommended or required readings
Platform(s) used for course materials:
- Microsoft Teams
Further information:
Students will have access to methodological guides and worksheets for image processing and analysis, as well as to databases of satellite and drone imagery.
They will also receive access to the EOSystM laboratory GitLab, which contains various R and Python scripts, and will be able to use the laboratory's drone equipment and specialized software for data processing.
Students will be put in contact with the laboratory's researchers, allowing them to benefit from their expertise and engage in direct discussions on specific methodological aspects related to ongoing research.
Written work / report
Further information:
Report writing and presentation: Students are expected to produce a structured report following the standard format of a scientific article (abstract, introduction/background, methodology, results, discussion, conclusion, and perspectives), ideally written in English.
Code archiving: All scripts and code developed as part of the project must be submitted and archived in the EOSystM laboratory GitLab.
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Prof. François Jonard, francois.jonard@uliege.be
Earth Observation and Ecosystem Modelling Lab - EOSystM - www.eosystm.uliege.be
Office 2/48, Building B5a, Quartier Agora, Allée du six Août 19, 4000 Liège