. "Geographic Information Science"@en . . "Remote Sensing"@en . . "Computer Science"@en . . "English"@en . . "Orientation project"@en . . "6" . "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. nhance 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." . . "Presential"@en . "TRUE" . . "Methods in geoinformatics"@en . . "12" . "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\ninformation extraction from large (‘big’) data sets, including flow of data, DBMS aspects and pattern analysis." . . "Presential"@en . "TRUE" . . "Spatial analysis and modelling"@en . . "6" . "Be able to map conceptual spatial relations (topological and geometrical) to the body of analytical methods.\n Recognize the value of different metrics in the spatial as well as attribute domains (e.g. fuzzy algebra).\n Describe shape characteristics of spatial features as well as complex landscape structures with the aim of diagnosing change.\n Identify, select (including SQL clauses) and statistically describe spatial features based and their distance to and/or topological relations with a target feature.\n Estimate values of a continuous (real or thematic) surface based on sample points through interpolation methods.\n Select adequate interpolation methods (based on characteristics of surface theme, measurement level, sample density) and assess quality of results.\n Derive characteristics of continuous surfaces as a basis for advanced models.\n Develop and adequately parameterized basic models of surface runoff, groundwater dynamics, visibility, solar irradiation and diffusion / spreading over inhomogeneous surfaces.\n Apply topological relations for combination of spatial themes (overlay analysis), derive and implement weighting schemes.\n Find best routes (paths across fields and networks.\n Allocate areas and features to service centres, distinguish from (‘optimal’) location analysis.\n Choose classification and regionalization methods according to specific requirements and contexts.\n Design, implement and validate complex workflows and process models built from individual methods and operations.\n Move from data analysis to generation of context-specific information and the creation of higher level domain knowledge." . . "Presential"@en . "TRUE" . . "Geo-application development"@en . . "12" . "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.\n 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.\n 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." . . "Presential"@en . "TRUE" . . "Spatial data infrastructure"@en . . "12" . "Be able to describe the main components of SDIs and know key objectives, benefits and current state-of-the-art of such initiatives.\n Understand the conceptual strategies, organizational requirements and legal frameworks for leveraging the advantages of open geographic data infrastructures.\n Recognize the importance of standardized data models to store, analyse and manipulate geographic phenomena.\n Be able to explain the role of metadata for spatial data shar-ing across distributed networks.\n Be able to describe the existing spatial data sharing policies including intellectual property rights, security issues, privacy issues, Open Government data initiatives.\n Be able to explain the Service Oriented Architecture (SOA) concept together with its underlying publish-find-bind principle.\n Know internationally accepted geographic- and IT standards (OGC, OASIS & ISO) and apply these in practical projects.\n Be able to understand, design and implement geodata models according to standardised approaches.\n Be able to publish geodata and geoprocessing services over the web: map services, data services (editing, search, image service), and analytical services.\n Be able to define the interoperability needs beyond technical issues like direct access and industry standards on a legal, semantic and organizational level.\n Understand the principles and techniques of spatial data organization and apply these principles and techniques to design and build spatial databases.\n 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." . . "Presential"@en . "TRUE" . . "International summer school"@en . . "no data" . "Social integration of student cohort through groupwork and a fulltime residential setting.\nDeep dive into a specific topical domain with particular professional relevance.\nContact opportunity with practitioners from industry and application domains.\nExperience with hands-on field work and data acquisition." . . "no data"@en . "FALSE" . . "Spatial databases"@en . . "3" . "This class covers the most important concepts for setting up and \nmanaging database systems (relational databases), populating databases \nwith 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 \ntouches the basics of standardisation, security, data integrity, and \noptimisation.Some more recent trends coming from big data analytics such as NoSQL, real-time- and in-memory systems will be touched." . . "Presential"@en . "FALSE" . . "Digital earth: big earth data concepts"@en . . "2" . "Understand current trends of big data in remote sensing and\n its background as well as\n applying new concepts and approaches." . . "no data"@en . "FALSE" . . "Object-based Image analyis"@en . . "3" . "overall understanding of object-based image analysis as an advanded image understanding strategy\n– applying spatial concepts in image analysis, such as geometrical, form-related, context-related properties of objects\n– handling basic technical principles of image segmentation and object-based classification and validation." . . "Online"@en . "FALSE" . . "Geohumanitarian actions"@en . . "4" . "Link causes and traits of humanitarian emergencies with the potential of geospatial monitoring capabilities\n– Oversee the variety of geospatial tools that are used on different operational levels (NGOs, GOs, community at large)\n– Understand both opportunities and challenges of latest geospatial technology in humanitarian action\n– Practice and collaborate in the context of Z_GIS Humanitarian Services" . . "Presential"@en . "FALSE" . . "Artificial intelligence"@en . . "6" . "understand the different machine learning problems and methods;\n design for a given data analytics problem the appropriate solution to be used;\n implement deep learning models within a standard framework." . . "Presential"@en . "TRUE" . . "Computer vision"@en . . "6" . "understand advanced models and techniques for image processing;\n solve realistic problems in computer vision." . . "Presential"@en . "TRUE" . . "Big data"@en . . "6" . "understand the principles of knowledge discovery and the methods for data mining;\nuse software framework to design, implement and deploy a solution for big data analytics" . . "Presential"@en . "TRUE" . . "Active and multitemporal remote sensing"@en . . "3" . "understand the principles of active and multitemporal remote sensing;\n remember of opportunities offered those recent sensors available in remote sensing;\n process the data provided by such sensors;\n perform data analysis to address specific methodological tasks;\n use dedicated software." . . "Presential"@en . "TRUE" . . "Interactive data vizualization"@en . . "3" . "understand main concepts behind human-computer interaction;\n design effective GUI;\n elaborate visualization strategies to ease understanding of the data." . . "Presential"@en . "TRUE" . . "Geodata science practical workshop"@en . . "6" . "use their technical skills to solve a real-world challenge\n develop an end-to-end solution, innovate and collaborate" . . "Presential"@en . "FALSE" . . "Principles of geovisualization"@en . . "6" . "explain the geovisualization process;\n create visualizations using and combining spatial and non-spatial data;\n evaluate visualization approaches of spatial data and build new ones upon the theoretical framework;\n analyze and categorize available techniques in terms of quality, efficiency, and suitability for a particular data type,\n evaluate available tools based on their functionality, and apply these tools to create own geovisualizations." . . "Presential"@en . "TRUE" . . "Thematic cartography"@en . . "6" . "compare different methods of thematic cartography;\n create thematic maps using various visualization techniques based on cartographic concepts and the general typographic guidelines;\n describe the relevance and influence of cartography to various associated fields." . . "Presential"@en . "TRUE" . . "Design in geovisualization"@en . . "6" . "understand current issues in design in geovisualization;\nevaluate design research approaches;\nanalyse and process geodata within a geovisualization context;\nevaluate different geovisualization techniques, principles and methodologies according to the applicability to the intended projec" . . "Presential"@en . "TRUE" . . "Web cartography"@en . . "6" . "demonstrate knowledge and skills in web cartography such as data processing, classification, visualization, and map design;\n produce different web maps or visualizations based on the aforementioned knowledge and skills;\n demonstrate good knowledge about web and mobile cartography such as Google maps, OpenStreetMap, and location-based services for mobile devices;\n evaluate and analyse the suitability of various cartographic formats for set purposes and audiences." . . "Presential"@en . "TRUE" . . "3d visualization"@en . . "4" . "remember advantages of applications of 3D data models;\n consider the range of 3D design options;\n choose suitable input data for 3D landscape models;\n understand necessity of quality and consistency control;\n use modelling software for own model preparation including texture;\n combine 3D content into a simple model;\n produce 3D content of moderate complexity;\n integrate 3D printing and virtual reality workflows." . . "Presential"@en . "FALSE" . . "Systematic geovisualization"@en . . "3" . "apply advanced cartographic theories and key criteria for developing geovisualization research projects;\n apply concepts, methods and methodologies of spatial data handling;\n evaluate and judge influencing factors of geovisualization projects in the context of a spatial data infrastructure;\n create components and relations of contemporary scientific geovisualization projects in the realm of SDI;\n evaluate systematic geovisualization research approaches;\n analyse and process geodata within a systematic context;\n combine spatial data with other non-spatial data within systematic geovisualization." . . "Presential"@en . "FALSE" . . "Field mapping"@en . . "3" . "evaluate different cartographic techniques, principles and methodologies according to the applicability to the field project;\n create user- and purpose-oriented results for the field project;\n discuss and present the applied cartographic/design methodologies with/to experts." . . "Presential"@en . "FALSE" . . "Advanced vizualization methods"@en . . "4" . "understand the relations of advanced visualization methods to associated fields;\n understand the fundaments in advanced visualization methods;\n understand key criteria’s for developing visualization research projects;\n create advanced visualization methods applications using contemporary programming languages and frameworks;" . . "Presential"@en . "FALSE" . . "Cognitive cartography"@en . . "4" . "understand the role of cognitive cartography in cartographic research;\n explain theories of perception and effective user-driven map-design;\n understand current cognitive research issues in relations to cartographic research questions;\n evaluate perception aand cognition of cartographic products;\n analyse the process of map reading;\n evaluate different cartographic techniques, principles and methodologies according to the cognitive processes;\n create user- and purpose-oriented results for the intended project." . . "Presential"@en . "FALSE" . . "Copernicus Master in Digital Earth"@en . . "https://master-cde.eu/" . "120"^^ . "Presential"@en . "The two year full-time integrated programme aims at qualifying individuals to lead initiatives, projects and institutions translating Copernicus data (remote sensing and in-situ) into information for management decisions within a broader Digital Earth vision.\n\nStudy Earth Observation and Geoinformatics in the first academic year. Two Specialization Tracks offer outstanding candidates a pathway towards excellence in GeoData Science as well as GeoVisualization and Geocommunication in the second year of studies. The Geographic Information Science / Systems (GIS) Master of Science programme is offered as double degree in English language."@en . . . . "2"@en . "TRUE" . . "Master"@en . "Thesis" . "4000.00" . "Euro"@en . "5000.00" . "Mandatory" . "no data"@en . "3"^^ . "TRUE" . "Downstream"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . "Department of Geoinformatics - Z_GIS"@en . .