Modelling and Data Interpretation
| Type of study | Bachelor |
|---|---|
| Language of instruction | English |
| Code | 544-0170/02 |
| Abbreviation | MID |
| Course title | Modelling and Data Interpretation |
| Credits | 5 |
| Coordinating department | Department of Geodesy and Mine Surveying |
| Course coordinator | doc. Ing. Roman Kapica, Ph.D. |
Subject syllabus
1. Spatial Data and Geospatial Data – basic concepts, types, and data sources.
2. Structure and Formats of Spatial Data – vector, raster, imagery, and 3D data.
3. Integration of Spatial Data – transformations, data integration, coordinate reference systems, and metadata.
4. Interpretation of Imagery and Raster Data – terrestrial and aerial imagery, and derived image products.
5. Interpretation of Point Clouds and 3D Data – preprocessing, filtering, and classification.
6. Integration and Analysis of Spatial Data – combining data from different sources and analysing spatial relationships.
7. Fundamentals of Mathematical Modelling – principles and applications in spatial data processing.
8. Terrain and Surface Modelling – digital terrain and surface models.
9. 3D Modelling of Spatial Data – creation and representation of 3D models.
10. Automated Classification and Object Extraction – segmentation, classification, and object detection.
11. Artificial Intelligence and Machine Learning – analysis and interpretation of spatial data.
12. Modelling and Interpretation for Geodetic and Engineering Applications – use of spatial data.
13. Visualization and Virtual Models – 3D visualization, virtual reality, and digital representations.
2. Structure and Formats of Spatial Data – vector, raster, imagery, and 3D data.
3. Integration of Spatial Data – transformations, data integration, coordinate reference systems, and metadata.
4. Interpretation of Imagery and Raster Data – terrestrial and aerial imagery, and derived image products.
5. Interpretation of Point Clouds and 3D Data – preprocessing, filtering, and classification.
6. Integration and Analysis of Spatial Data – combining data from different sources and analysing spatial relationships.
7. Fundamentals of Mathematical Modelling – principles and applications in spatial data processing.
8. Terrain and Surface Modelling – digital terrain and surface models.
9. 3D Modelling of Spatial Data – creation and representation of 3D models.
10. Automated Classification and Object Extraction – segmentation, classification, and object detection.
11. Artificial Intelligence and Machine Learning – analysis and interpretation of spatial data.
12. Modelling and Interpretation for Geodetic and Engineering Applications – use of spatial data.
13. Visualization and Virtual Models – 3D visualization, virtual reality, and digital representations.
E-learning
Individual assignments will be made available via MS Teams.
Literature
SILVA, Irineu da a SEGANTINE, Paulo Cesar Lima. Geomatics applied to civil engineering. Second edition. Cham, Switzerland: Springer, [2025]. ISBN 978-3-031-75736-5.
GRUBESIC, Tony H. a NELSON, Jake R. UAVs and urban spatial analysis: an introduction. Cham, Switzerland: Springer, [2020]. ISBN 978-3-030-35864-8.
TYCHOLA, Kyriaki A., Eleni VROCHIDOU a George A. PAPAKOSTAS. Deep learning based computer vision under the prism of 3D point clouds: a systematic review. The Visual Computer. 2024, 40, 8287–8329. DOI 10.1007/s00371-023-03237-7.
IOANNIDES, Marinos a Petros PATIAS (eds.). 3D Research Challenges in Cultural Heritage III: Complexity and Quality in Digitisation [online]. Cham: Springer, 2023. ISBN 978-3-031-35592-9 . DOI 10.1007/978-3-031-35593-6. Dostupné z: https://link.springer.com/book/10.1007/978-3-031-35593-6 [cit. 2026-08-21].
GRUBESIC, Tony H. a NELSON, Jake R. UAVs and urban spatial analysis: an introduction. Cham, Switzerland: Springer, [2020]. ISBN 978-3-030-35864-8.
TYCHOLA, Kyriaki A., Eleni VROCHIDOU a George A. PAPAKOSTAS. Deep learning based computer vision under the prism of 3D point clouds: a systematic review. The Visual Computer. 2024, 40, 8287–8329. DOI 10.1007/s00371-023-03237-7.
IOANNIDES, Marinos a Petros PATIAS (eds.). 3D Research Challenges in Cultural Heritage III: Complexity and Quality in Digitisation [online]. Cham: Springer, 2023. ISBN 978-3-031-35592-9 . DOI 10.1007/978-3-031-35593-6. Dostupné z: https://link.springer.com/book/10.1007/978-3-031-35593-6 [cit. 2026-08-21].
Advised literature
SCHIEWE, Jochen. Cartography: Visualization of Geospatial Data. Springer Textbooks in Earth Sciences, Geography and Environment. Cham: Springer, 2025. ISBN 978-3-031-83022-8.
MAUNE, David F. a Amar NAYEGANDHI (eds.). Digital Elevation Model Technologies and Applications: The DEM Users Manual. 3rd ed. Bethesda, MD: American Society for Photogrammetry and Remote Sensing, 2018. ISBN 978-1-57083-102-7.
BEIL, Christof a Thomas H. KOLBE. Applications for Semantic 3D Streetspace Models and Their Requirements—A Review and Look at the Road Ahead. ISPRS International Journal of Geo-Information. 2024, 13(10), 363. DOI 10.3390/ijgi13100363.
ABREU, Nuno, Andry PINTO, Aníbal MATOS a Miguel PIRES. Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review. ISPRS International Journal of Geo-Information. 2023, 12(7), 260. DOI 10.3390/ijgi12070260.
MAUNE, David F. a Amar NAYEGANDHI (eds.). Digital Elevation Model Technologies and Applications: The DEM Users Manual. 3rd ed. Bethesda, MD: American Society for Photogrammetry and Remote Sensing, 2018. ISBN 978-1-57083-102-7.
BEIL, Christof a Thomas H. KOLBE. Applications for Semantic 3D Streetspace Models and Their Requirements—A Review and Look at the Road Ahead. ISPRS International Journal of Geo-Information. 2024, 13(10), 363. DOI 10.3390/ijgi13100363.
ABREU, Nuno, Andry PINTO, Aníbal MATOS a Miguel PIRES. Procedural Point Cloud Modelling in Scan-to-BIM and Scan-vs-BIM Applications: A Review. ISPRS International Journal of Geo-Information. 2023, 12(7), 260. DOI 10.3390/ijgi12070260.