The primary goal of the subject is to obtain basic theoretical and practical knowledge of sensors and algorithmic tools used in spatial information systems focusing on applications of remote sensing and medical imaging. The course provides an overview of the operating principles of the various 3D sensing technologies and their areas of use, the data structures describing the provided measurements, with a special emphasis on the automated data processing methods and algorithms, which nowadays play a prominent role due to the heterogeneity of the available data, and the large and continuously expanding size of current spatial databases.

Main topics:

1. Concept of spatial information sciences, main types and applications of spatial information systems

2. Operation principles of geospatial and medical 3D data collection sensor technologies and tools

3. Geometric reference systems, spatial geometric transformations, projective geometry

4. Formation and representation of various data types: 2D images (optical visible/infrared, radar, X-ray images), 2.5D distance maps, real 3D Lidar point clouds and 3D voxel models (e.g. CT, MR)

5. Geospatial and medical databases: data acquisition and data storing models

6. Data quality standards and quality assurance. Hardware and software too

ls in spatial information system, database management issues.

7. Automatic processing and interpretation of spatial data sets: segmentation of data from different sources, separation of different classes, recognition and reconstruction of spatial objects

8. Hierarchical description of complex spatial scenes, stochastic optimization techniques (Markov fields, Markov point processes)

9. Automatic 3D model generation from raw measured data, processing of 3D point clouds (e.g. LIDAR) and voxel models (e.g. computed tomography).

10. Time sequence analysis, registration, and change detection for multitemporal data

11. Basic concepts of augmented and mixed reality systems

Required and recommended literature: