The first academic year at the University of Salzburg (PLUS) includes following courses:



Learning Outcomes: Upon completion of the module, students are able to:

  • build adequate expectations and adjust to the requirements of the MSc CDE programme.
  • compensate any deficiencies from their undergraduate studies, particularly in the areas of informatics / computing as well as basic GIS skills, basic spatial literacy and cartographic competences, fundamental understanding of spatial sciences and general quantitative methods.
  • enhance their general orientation in scientific methods and scientific writing in a dedicated set of classes, as a preparation for supervised and independent work in advanced classes.
  • establish their individual ePortfolio.

Module content:

  • Orientation regarding structure of entire curriculum and student life at CDE partner universities.
  • Integration with student cohort.
  • Perspectives on professional outlook and career development.
  • Planning and design of one’s individual course of study, including specific methodology and / or domain emphases.
  • Personal SWOT analysis and translation of outcomes into action.
  • Written communication in science.
  • Structuring of documents according to media and target audience.
  • Scientific writing in English language.
  • Adequate use and referencing of sources, empirical evidence and pertinent tools.
  • Elementary research design.
  • Professional ethics.

Type of exam:  Submission of several individual and group mini-projects aiming at orientation, social environments and geomedia / geospatial communication. These projects serve as assignments graded from a combination of peer and teacher assessment.

The course is taught as Interdisciplinary Project (IP/Orientation Project) which integrates approaches, concepts and methods from various disciplines for holistic problem solving across disciplines, including practical as well as conceptual synergies. Course participation is continuously assessed and attendance is mandatory.

Learning Outcomes: Upon completion of the module, students are able to:

  • apply the selected methods in project-oriented work and take methodological responsibilities in working groups and complex workflows.

Depending on individual choices, students will:

  • Design and implement advanced geovisualisation interfaces for use-case oriented media, devices and user experiences.
  • Decide on adequate Remote Sensing data sources and workflows across available passive and active sensors.
  • Apply the Object-Based Image Analysis (OBIA) paradigm to the extraction of features and monitoring of change across remote sensing application domains.
  • Select and implement advanced geodata acquisition processes using e.g. photogrammetry, LiDAR, in-situ and mobile sensors, crowdsourcing and UAV platforms, including real-time data streams.
  • Prepare and support decisions through (geo-)simulation.
  • Choose and apply spatial- and geo-statistical methods to analyse multidimensional and multivariate data sets to explain and model complex relations and processes.
  • Manage information extraction from large (‘big’) data sets, including flow of data, DBMS aspects and pattern analysis.

Module content:

  • A selection of core geoinformatics methodologies like remote sensing, geovisualisation or data analysis, sharpening personal competence profiles in combination with choices in electives, IP courses, seminar and thesis topics.
  • All courses have a strong practice orientation, combining conceptual foundations with a view towards applications.

Depending on courses, chosen content will vary and include combinations from:

  • Remote Sensing – field and mobile data acquisition. Advanced sensors. Hyperspectral and Microwave analysis. Radiometric correction.
  • OBIA with transferable rules and app development.
  • Geovisualisation – use case analysis and UX design. Design of flexible and responsive interfaces. Navigation of perspective views.
  • Data and process analysis – advances spatial statistics and pattern analysis. Geostatistics. Big data analysis. Process simulation with individual based vs aggregate/lumped approaches.

Type of exam: Teacher and peer assessment of individual assignments, optionally presentations and portfolio entries, plus overview tests.

All courses are taught as practicals, fostering problem-oriented and experiential learning through individual or group assignments.
– Advanced Remote Sensing
– Multivariate Statistics | Spatial Statistics | Geostatistics
– Geovisualization and Advanced Cartography
– Geodata Acquisition
– Modeling Geographical Systems, Spatial Simulation
– Location Based Services, Big Data Analytics

Learning Outcomes: Upon completion of the module, students are able to:

  • build advanced translation skills from application domain problems towards conceptual reframing and structuring, and into the analytical methods and toolsets of Geoinformatics.

Based on this knowledge of operational methods, complete workflows representing complex processes are modeled and represented in structured frameworks for spatial decision support across domains. Students will:

  • Be able to map conceptual spatial relations (topological and geometrical) to the body of analytical methods.
  • Recognize the value of different metrics in the spatial as well as attribute domains (e.g. fuzzy algebra).
  • Describe shape characteristics of spatial features as well as complex landscape structures with the aim of diagnosing change.
  • Identify, select (including SQL clauses) and statistically describe spatial features based and their distance to and/or topological relations with a target feature.
  • Estimate values of a continuous (real or thematic) surface based on sample points through interpolation methods.
  • Select adequate interpolation methods (based on characteristics of surface theme, measurement level, sample density) and assess quality of results.
  • Derive characteristics of continuous surfaces as a basis for advanced models.
  • Develop and adequately parameterized basic models of surface runoff, groundwater dynamics, visibility, solar irradiation and diffusion / spreading over inhomogeneous surfaces.
  • Apply topological relations for combination of spatial themes (overlay analysis), derive and implement weighting schemes.
  • Find best routes (paths across fields and networks.
  • Allocate areas and features to service centres, distinguish from (‘optimal’) location analysis.
  • Choose classification and regionalization methods according to specific requirements and contexts.
  • Design, implement and validate complex workflows and process models built from individual methods and operations.
  • Move from data analysis to generation of context-specific information and the creation of higher level domain knowledge.

Module content:

  • Topological relationships (Egenhofer). Map Algebra. Distance metrics. Spatial query operators. Fuzzy metrics and algebra. Shape and landscape metrics.
  • Interpolation methods (trend surface, IDW, … and cross reference to statistical methods like Kriging).
  • Surface descriptors. Spatial models with gravity and radiative mechanisms. Cost surface modeling.
  • Network: Dijkstra algorithm. Vector and raster overlay, incl. weighted overlay and AHP. Allocation and location analysis. Nodal and homogeneous regionalization.
  • Process model building.
  • Spatial decision support strategies.

Type of exam: Assessment of individual lab assignments plus overview test. Presentation of seminar (project) paper with peer and teacher assessment.

Through a combination of a practical class including extensive lab components with an advanced seminar, students develop broad competences across the spectrum of analytical methods (optionally including spatial statistical and remote sensing methods), as well as a deeper understanding and critical appreciation of results through application experience of selected methods and their parameterization contexts.
– PS Methods in Spatial Analysis
– SE Analysis and Modeling

Learning Outcomes: Upon completion of the module, students

  • gain a well-structured understanding of software development from a software engineering (SWE) perspective, enabling them to work as geospatial experts in development teams and to successfully communicate with software developers.
  • acquire competences in at least two development environments and languages, enabling them to design simple software programs, to customize existing applications, and to automate basic workflows. This includes practical skills in geo-application development in the areas of web applications, mobile applications, or desktop analytical applications.
  • are able to carry out basic development tasks on a variety of platforms and architectures with an emphasis on understanding and translating demands from typical geospatial application domains.

Students will be able to:

  • Design and carry out software projects in accordance with standardized and structured SWE processes.
  • Select the appropriate programming or scripting language according to the specific goals of a software project.
  • Apply their basic knowledge of modeling software systems for communication between different stakeholders in a SWE project.
  • Programmatically access external code libraries and Application Programming Interfaces (APIs) of commercial off-the-shelf (COTS) and open source software in their own programs to achieve their goals.
  • Develop software programs to pre-process and analyze spatial data (read, manipulate, store, visualize, classify) that are available in a variety of formats (CSV, ShapeFiles, GML, KML, raster formats etc.).
  • Integrate data from service-oriented architectures (SOA), including OGC Web Services (OWS) into their software programs through service-based data access.
  • Read and understand the documentation of software libraries.
  • Create user interface components in selected development environments.
  • Batch analysis tasks in the application domains of GIS and remote sensing.
  • Develop geo-applications for different platforms (desktop, web, mobile, …) and application domains (GIS, remote sensing).

Module content:

  • Principles of software engineering.
  • Procedural and object-oriented programming principles.
  • Approaches to modeling software systems using UML.
  • Service-oriented Architectures. OGC Web Services (OWS).
  • Client-side and server-side scripting languages (e.g., JavaScript, Python, or similar).
  • Object-oriented programming vs. scripting.
  • Server-side OO programming and scripting (e.g. JSP, Python, PHP, or similar).
  • Programmatic database access.
  • Program development for spatial data pre-processing.
  • APIs in commercial off-the-shelf (COTS) and/or open source software.
  • Web Mapping. Web GIS.
  • Batch processing for GIS and remote sensing analysis and classification tasks.
  • Basic GUI design.

Type of exam: Assessment of individual lab assignments plus overview test. Presentation of focus topic with peer and teacher assessment. Major development project in one of the selected IPs.

Through a combination of an introductory lecture and a lab exercise as well as an IP (selectable from different application domains) including extensive practical components, students develop broad competences across the spectrum of application development methods on different platforms and programming languages (at least two) as well as different application domains (optionally including remote sensing applications).
– VO Basics of Software Development
– PS Practice: Software Development
– Selectable IPs: IP Application Development (web|mobile|desktop) ; IP Application Development (remote sensing applications)

Learning Outcomes: Upon completion of the module, students will:

  • Be able to describe the main components of SDIs and know key objectives, benefits and current state-of-the-art of such initiatives.
  • Understand the conceptual strategies, organizational requirements and legal frameworks for leveraging the advantages of open geographic data infrastructures.
  • Recognize the importance of standardized data models to store, analyse and manipulate geographic phenomena.
  • Be able to explain the role of metadata for spatial data shar-ing across distributed networks.
  • Be able to describe the existing spatial data sharing policies including intellectual property rights, security issues, privacy issues, Open Government data initiatives.
  • Be able to explain the Service Oriented Architecture (SOA) concept together with its underlying publish-find-bind principle.
  • Know internationally accepted geographic- and IT standards (OGC, OASIS & ISO) and apply these in practical projects.
  • Be able to understand, design and implement geodata models according to standardised approaches.
  • Be able to publish geodata and geoprocessing services over the web: map services, data services (editing, search, image service), and analytical services.
  • Be able to define the interoperability needs beyond technical issues like direct access and industry standards on a legal, semantic and organizational level.
  • Understand the principles and techniques of spatial data organization and apply these principles and techniques to design and build spatial databases.
  • Based on these concepts, the students will learn how to utilize open, shared GIS resources to design and use Open GIS data structures, workflows and processes leveraging information repositories.

Module content:

  • Conceptual foundations: Geographic information reference model, spatial schema, temporal schema, spatial referencing; spatial relationships, functions and operations; Interoperability (syntactic, semantic and technical); distributed IT architectures (private/public cloud, Internet of Things etc.); spatio-temporal information integration; spatial data infrastructure concepts (service-orientation; publish-find-bind principle; semantic web).
  • Technological Foundations: Geospatial data modelling (UML, GML); application schema; GI Ontologies; domain bridging data Integration; Geospatial Data Management (Simple feature ac-cess, 13249-3 Information technology – SQL Multimedia and Application-Part 3); Spatial DBMS: Oracle Spatial, MSSQL Spatial, Postgres/PostGIS, ESRI ArcSDE etc.; geospatial net-work-service architectures (view, download, discovery & registry, web processing and security services); Communicating with WebGIS; GI applications services using COTS and open-source solutions; private/public cloud-computing platforms; data & metadata repositories; Big GI data & Geospatial Eventing.
  • Standards and Regulations for Interoperability: ISO/TC211 19100 standards series, Open Geospatial Consortium; Legal acts: Laws on SDIs, Environmental INSPIRE, Public Sector Information INSPIRE Directives; privacy and security issues.
  • Initiatives: Open Government Data; GSDI-Global Spatial Data Infrastructure, GEOSS-Global Earth Observation System of Systems

Type of exam: Written exams for the lectures. IP: hands-on project work with strong motivation from real world problems; detailed documentation according to corresponding standards; Evaluation of the approach to challenge in the course of the project as well as the final results.

The courses are taught as Lecture (VO) which provides an overview of a subject or one of its sub-areas and its theoretical approaches and presents different teachings and methods. The contents are mainly presented in lecture style. Attendance is not mandatory but highly recommended. Or as Pro-seminar (PS) which is a scientifically oriented course in preparation for seminars. Students acquire fundamental knowledge and skills for scientific research through practical as well as conceptual work. Course participation is continuously assessed and attendance is mandatory. Or as Interdisciplinary Project (IP) which integrates approaches, concepts and methods from various disciplines for holistic problem solving across disciplines, including practical as well as conceptual synergies. Course participation is continuously assessed and attendance is mandatory.
– Design of Geospatial Data Models
– Open GIS: Standards, Architectures and Services
– SDI Services Implementation

Learning Outcomes:  As a core element in an international study programme integrating students from very diverse backgrounds and pursuing different pathways, participating in a summer school aims at several important objectives:

  •  Social integration of student cohort through groupwork and a fulltime residential setting.
  • Deep dive into a specific topical domain with particular professional relevance.
  • Contact opportunity with practitioners from industry and application domains.
  • Experience with hands-on field work and data acquisition.

Module content: Depending on the chosen topic (summer schools will be offering a variety of themes), the content will allow students to build a holistic understanding of the respective theme through an immersive experience

Type of exam: Integrated, continuous assessment including group work (depending on summer school theme) and individual written and optionally oral presentation of assigned topic.

Summer schools will be offered across the entire partnership, in addition the Programme Board will identify additional SS opportunities worldwide.


Learning Outcomes: Upon completion of the course, students are able to:

  • operate a spatial database system (in particular PostgreSQL),
  • use it to manage and analyse spatial data using spatial operations of the database, and
  • understand the role of a database system within a GI infrastructure.


  • This class covers the most important concepts for setting up and managing database systems (relational databases), populating databases with data and executing SQL queries.
  • Basic topics about relational databases in general are covered in the first part.
  • The second part focuses on spatial concepts, spatial databases and touches the basics of standardisation, security, data integrity, and optimisation.
  • Some more recent trends coming from big data analytics such as NoSQL, real-time- and in-memory systems will be touched.

Type of exam: continuous assessment

The class is a mixture of lectures and hands-on sessions. While the main content will be independent of a specific database system, the PostgreSQL database management system and QGIS will be used. Course participation is continuously assessed and attendance is mandatory. References to relevant literature will be provided.

Learning Outcomes: Upon completion of the course, students are able to:

  • Understand current trends of big data in remote sensing and
  • its background as well as
  • applying new concepts and approaches.


  • Recent trends in Earth observation called “big Earth data”
  • covers or touches topics such as accessing knowledge-based systems & machine learning and processing of massive amount of big data, data cubes, artificial intelligence (knowledge-based systems; machine learning) and possible application areas for continental- or global-scale remote sensing image processing.

Type of exam: continuous assessment

The class is taught as mixture of theoretical and hands-on sessions. Course participation is continuously assessed and attendance is mandatory. References to relevant literature will be provided.

Learning Outcomes: Upon completion of the course, students will have acquired the following competences:

– overall understanding of object-based image analysis as an advanded image understanding strategy
– applying spatial concepts in image analysis, such as geometrical, form-related, context-related properties of objects
– handling basic technical principles of image segmentation and object-based classification and validation.

Content: The increasing availability of very high resolution satellite data (e.g. WorldView, Pleiades, but also drones etc.) as well as high-frequent satellite data (e.g. Sentinel-2) require new approaches for image processing, interpretation and analysis in order to exploit imaged content more effectively. Object-based image analysis (OBIA) provides methods and tools for multi-scale representation and class modelling by integrating spatial concepts and knowledge-based strategies for advanced image understanding. At the interface between GIS and remote sensing technologies, OBIA offers a powerful approach for utilizing image information for various application fields.

Chapters include:
– Why OBIA?
– Image interpretation and perception
– Basic concepts of hierarchy theory
– Knowledge representation
– Image segmentation
– Object-based classification (incl. class modelling)
– Accuracy assessment (incl. object validity)
– OB(I)A for non-image data

Type of exam: continuous assessment

elearning sync
The course provides self-explanatory online material, embedded in an e-learning platform. The course is taught via video/audio tracks. You’ll have to have Flash Player (browser-integrated) and a sound device installed. All relevant aspects on Copyright, IPRs, etc. are discussed in the Introduction. Supervision is granted through the online discussion board.

Learning Outcomes: Upon completion of the course, students will have acquired the following competences:

– Link causes and traits of humanitarian emergencies with the potential of geospatial monitoring capabilities
– Oversee the variety of geospatial tools that are used on different operational levels (NGOs, GOs, community at large)
– Understand both opportunities and challenges of latest geospatial technology in humanitarian action
– Practice and collaborate in the context of Z_GIS Humanitarian Services

Content: Spatial awareness and literacy, and a boost in proliferating geospatial technologies, are crucial ingredients to nowaday’s effective operations in humanitarian action. This applies to first responders in disasters, humanitarian relief, witnessing and human rights, conflict prevention and more. Geospatial tools, including web and field mapping, Earth browsers and VGIs, VHR satellite data and drones, will be discussed with respect to their potential for the humanitarian action domain.
This course will enable students to aquire a ‘certificate’ to collaborate in the GeoHumanitarian Action Team established at Z_GIS and Spatial Services.

Type of exam: continuous assessment

elearning sync

latest update: February 25, 2020