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103-0251-00L 4 Credits MSC D-BAUG , D-MAVT , D-PHYS , D-ERDW , D-ITET

Computational Methods for Geospatial Analysis

Lecturers & Examiners: Prof. Dr. Konrad Schindler, Prof. Dr. Andreas Wieser, Prof. Dr. Benedikt Soja, Tianyi Xiao

VVZ CR n/a

Last Updated: 2026-06-03 00:07:45

Abstract

Introduction to mathematical and statistical tools for geospatial data analysis.

Objective

The 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.

Content

The 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.

General Information

Language: English
Levels: MSC
Frequency: Yearly recurring

Examination

Type: graded semester performance

The grade is determined through continuous performance assessment during the semester, in the form of programming assignments.

Course Components

Type	Title	Time & Place	Hours
lecture with exercise	Computational Methods for Geospatial Analysis	No time listed	4 h weekly

Offered In

Geomatics Master
- Master Studies (Programme Regulations 2022)
  - Compulsory Courses
Spatial Development and Infrastructure Systems Master
- Master Studies (Programme Regulations 2021)
  - Major Courses
    - Major in Spatial and Landscape Development
Space Systems Master
- Deep Track Courses (At least 20 credits must be completed within the deep track courses. Surplus credit points can be counted towards the electives.)
  - Earth Observation
    - Elective Courses Earth Observation (These subjects can only be credited as electives.)