Search result: Catalogue data in Autumn Semester 2023

Geomatics Master Information
Master Studies (Programme Regulations 2022)
Compulsory Courses
NumberTitleTypeECTSHoursLecturers
103-0248-00LGeospatial Research MethodsO4 credits4GM. Raubal
AbstractThe goal of this seminar-style course is to convey methods how to do research and communicate research results in the geospatial domain. The course further provides an overview of the types of research in the geospatial domain and the research life cycle.
Learning objectiveStudents will exercise important aspects when doing research, such as doing a literature search, writing and referencing, and presenting.
103-0249-00LGeospatial Reference SystemsO4 credits4GA. Wieser, M. Varga
AbstractThis course is an advanced introduction to spatial and temporal reference systems for acquisition, analysis and communication of geospatial data. The course covers definitions, conventions and comprehensive real world examples of coordinate reference systems, time reference systems, their respective practical realization, and operations for changing data between them.
Learning objectiveAfter this course the students should be able to

describe the most important established national and international spatial and temporal reference systems;
describe the techniques, processes, and institutions needed to establish and maintain reference frames;
select appropriate reference systems and frames for specific geospatial modeling/analysis tasks;
carry out coordinate transformations, conversions, and time operations on geospatial data, taking into account and quantifying the uncertainties;
combine geospatial data originally referring to different reference frames into a single reference frame.
Prerequisites / NoticeThe course requires familiarity with linear algebra and analysis at the level of a BSc program in engineering or natural sciences.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingfostered
Personal CompetenciesCritical Thinkingassessed
103-0250-00LGeospatial Data AcquisitionO4 credits4GA. Wieser
AbstractThis course supports the students in acquiring an in-depth understanding of sensors, sensor systems and sensor networks for the acquisition of geospatial data. Emphasis is put on the prediction and assurance of data quality based on an understanding of key sensing principles, external influences, and data acquisition processes.
Learning objectiveAfter this cours, the students should be able to

describe main sensing principles for time, distance, angle, position, attitude, motion, temperature, optical imaging and spectrum;
describe main performance criteria of sensors and sensor systems for static and dynamic geospatial applications;
control s ensors for geospatial data acquisition using a computer and self-written programs;
predict the performance of sensors and sensor systems based on information from data sheets and documentation of sensor system architecture;
assess the performance of sensors and sensor systems experimentally.
Prerequisites / NoticeThe course requires familiarity with linear algebra and analysis at the level of a BSc program in engineering or natural sciences.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingfostered
Problem-solvingfostered
Personal CompetenciesCreative Thinkingfostered
Critical Thinkingassessed
103-0251-00LComputational Methods for Geospatial AnalysisO4 credits4GK. Schindler, J. A. Butt, B. Soja, Y. Xin
AbstractIntroduction to mathematical and statistical tools for geospatial data analysis.
Learning objectiveThe goal is to familiarise students with the principles and tools of geospatial data analysis, and to enable them to apply those tools to practical tasks.
ContentThe course introduces basic methods of geostatistics and geospatial data analysis. Topics include spatial correlation, auto-correlation and the variogram; surface interpolation (kernel-based, kriging, parametric surface models); spatially adaptive filtering (bilinear, guided filter); spatial stochastic processes and random fields; time series models and spatio-temporal analysis.
Prerequisites / NoticeBachelor level mathematics: analysis, linear algebra, statistics and probability theory, parameter estimation. Basic knowledge of multivariate statistics and machine learning is recommended.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingfostered
Problem-solvingassessed
Personal CompetenciesCreative Thinkingfostered
Critical Thinkingfostered
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