Subject Datasheet
Completion requirements
Subject Datasheet
Download PDFI. Subject Specification
1. Basic Data
1.1 Title
Project Lab II.
1.2 Code
BMEEOFTMSFGG05-00
1.3 Type
Module with associated contact hours
1.4 Contact hours
| Type | Hours/week / (days) |
| Seminar | 1 |
1.5 Evaluation
Midterm grade
1.6 Credits
4
1.7 Coordinator
| name | Dr. Kapitány Kristóf |
| academic rank | Associate professor |
| kapitany.kristof@emk.bme.hu |
1.8 Department
Department of Photogrammetry and Geoinformatics
1.9 Website
1.10 Language of instruction
hungarian
1.11 Curriculum requirements
Compulsory in the Land Surveying and Geoinformatics (MSc) programme
1.12 Prerequisites
1.13 Effective date
1 September 2025
2. Objectives and learning outcomes
2.1 Objectives
The course aims to enable students to solve a geodetic or geoinformatics problem in a civil engineering context through project-based work in a topic relevant to their interest. During the project, students deepen their problem-solving skills, apply data processing and modelling methods, and develop towards independent research or design activities. The topic may be any problem agreed upon with the supervisor, including (but not limited to): field data collection, processing of laser scanning point clouds, remote sensing data analysis, image processing, or spatial data analysis and modelling related to building information systems.
2.2 Learning outcomes
Upon successful completion of this subject, the student:
A. Knowledge
1. Understands the theoretical background of the chosen problem.
2. Is familiar with the main literature related to the selected topic.
3. Knows up-to-date methods of spatial data acquisition, processing, and visualization.
4. Is aware of the integration opportunities between GIS and BIM.
5. Understands the geoinformatics needs of civil engineering applications.
6. Has knowledge of the data models and software tools necessary for the project work.
7. Understands quality assurance and reliability assessment methods for geospatial data.
8. Is aware of the advantages, limitations, and applicability of the chosen method.
B. Skills
1. Able to independently define problems and formulate research questions.
2. Capable of selecting appropriate IT tools for engineering problems.
3. Applies suitable software tools during the problem-solving process.
4. Can interpret, present, and document results obtained during the project.
5. Able to integrate and evaluate data from multiple sources.
C. Attitudes
1. Open to interdisciplinary issues, especially at the interface of civil engineering and geoinformatics.
2. Strives for precise, transparent, and well-documented work.
3. Shows interest in spatial data technologies and is willing to adopt new tools.
4. Continuously enhances knowledge through independent learning.
5. Aims for accurate and error-free task completion.
6. Responds constructively to supervisor feedback and integrates it into the project.
7. Develops digital competencies and adapts to new technologies.
D. Autonomy and Responsibility
1. Capable of independently planning and scheduling a small-scale research or development project.
2. Takes responsibility for all stages of the project—from data collection to interpreting results.
3. Makes decisions in complex, open-ended problems with the supervisor's guidance.
4. Aims to adhere to research ethics and professional norms, especially regarding the use and publication of geospatial data.
2.3 Methods
Individual project work with supervisor support. Weekly consultations are held in the chosen topic. The tools and software used depend on the project. Students regularly report their progress during the course.
2.4 Course outline
1. Introduction, course requirements, overview of project topic options.
2. Topic discussion, clarification of objectives and research questions; literature review methodology.
3. Finalization of topic selection; definition of objectives and expected outcomes.
4. Conclusion of literature review, methodological framework; interim summary 1.
5. Development of data acquisition and processing plan; selection of relevant software and tools.
6. Individual consultations according to project progress – start of data collection.
7. Consultation: preliminary results, issues, and possible solutions.
8. Interim summary 2 – presentation of results to date, methodological review.
9. Continued project work; data processing, modelling, and analysis.
10. Consultation: data interpretation, validation of results, documentation aspects.
11. Progress presentation 1 – project status and preliminary conclusions.
12. Refinement of project work, preparation of final materials, consultation.
13. Progress presentation 2 – final results, conclusions, reflections.
14. Summary, assessment criteria, final submission guidance.
The above programme is tentative and subject to changes due to calendar variations and other reasons specific to the actual semester. Consult the effective detailed course schedule of the course on the subject website.
The above programme is tentative and subject to changes due to calendar variations and other reasons specific to the actual semester. Consult the effective detailed course schedule of the course on the subject website.
2.5 Study materials
Topic-specific literature and software documentation
Case studies recommended by the supervisor
Downloadable support materials available on the course Moodle page
2.6 Other information
0
2.7 Consultation
At times agreed with the supervisor or according to departmental consultation schedules.
This Subject Datasheet is valid for:
2025/2026 semester II
II. Subject requirements
Assessment and evaluation of the learning outcomes
3.1 General rules
The learning outcomes defined in point 2.2 are assessed based on a written test, a homework assignment, and a presentation on a topic independently researched by the student. The minimum attendance requirement for students at contact hours is 70%.
3.2 Assessment methods
| Assessment Name (Type) | Code | Assessed Learning Outcomes |
|---|---|---|
| Home Work Assignment | HW1 | A.1-A.8; B.1-B.5 |
| Active Participation | A | A.1-A.8; B.1-B.5; C.1-C.7; D.1-D.4 |
The dates of deadlines of assignments/homework can be found in the detailed course schedule on the subject’s website.
3.3 Evaluation system
| Code | Weight |
|---|---|
| HW1 | 80% |
| A | 20% |
| Total | 100% |
3.4 Requirements and validity of signature
No signature can be obtained for this course.
3.5 Grading system
| Grade | Score (P) |
|---|---|
| excellent (5) | 85≤P |
| good (4) | 75≤P<85% |
| satisfactory (3) | 65≤P<75% |
| pass (2) | 50≤P<65% |
| fail (1) | P<50% |
3.6 Retake and repeat
The homework assignment may be submitted late—subject to the payment of the fee specified in the regulations—by 23:59 on the last day of the make-up period, in electronic form.
3.7 Estimated workload
| Activity | Hours/Semester |
|---|---|
| Participation in contact (in-person) classes | 14x2=28 |
| Project-related planning, preparation, and research | 20 |
| Implementation of the project (data processing, modelling, etc.) | 40 |
| Preparation of documentation and presentations | 20 |
| Independent study and reflection | 12 |
3.8 Effective date
1 September 2025
This Subject Datasheet is valid for:
2025/2026 semester II