. "Geospatial Analytics And Modelling"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Methods in spatial analysis"@en . . "2" . "Students therefore will be able to immediately apply the respective 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,\ncrowdsourcing 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.Mmultidimensional 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." . . "Presential"@en . "TRUE" . . "Analytical methods in geoinformatics"@en . . "7.5" . "Review of Elements of Probability Theory and Statistics. Distribution and density functions. Statistical tests. Transmission law of variables.\nTheory of least squares measurement correction. General and special cases. Special cases of solution of observation equations. Sequential addition of observations, correction of observations in phases, summation of normal equations.\nObservation equations with prior parameter estimates and/or parameter constraints, parameters with weights. Solution of systems of regular equations of large dimensions and/or special arrangements (tridiagonal, arrowhead, multi-banded)\nModelling of dynamic states, derivation of Kalman filter equations, aspects of their application. Statistical tests and analysis in least squares and Kalman filter methods.\nInterpolation and filtering methods. Various Kriging cases. Surface adaptations. Method of least squares collocation, relation to the least squares method." . . "Presential"@en . "TRUE" . . "Computational methods in geoinformatics"@en . . "7.5" . "Data Structures and Algorithms\nWhere data structures are needed, types of structures (tables, lists, trees, hash), algorithms for searching and sorting data, files.\nDatabases\nIntroduction to Databases (DB) - Fundamentals - Database Management Systems (DBMS) - Categories of DBMS - DBMS Architectures - Data Models - The relational model - Information System Design for DBMS (Imaginative Design, Logical Design, Physical Design). DB languages - The SQL language - Simple and complex questions. DBMS for small applications - The Microsoft Access package - Tables - Creating tables and associations - Questions (simple, statistical, etc.)\nAnalysis and processing of images\nCapture (capture) and Visualization (display) of images, Image structures and encoding (binary, quadtrees, etc.), Pattern recognition - statistical, syntactic, structural recognition.\nKnowledge Systems and Experiential Systems\nKnowledge representation. Basic principles and methodologies. Relationship between knowledge bases and databases. Experiential systems." . . "Presential"@en . "TRUE" . . "Applications of geoinformatics"@en . . "7.5" . "Data processing (data entry, digitisation, web integration, integration)\nGeodatabase design and development\nCreation of a Digital Terrain Model\nSpatial analysis - spatial queries (topology, network analysis, zoning)\nGeo-visualisation - Data/results rendering" . . "Presential"@en . "FALSE" . . "Expert systems in geoinformatics"@en . . "7.5" . "Introduction to Arificiall Intelligence. Categories of Artificial intelligence, supervised, unsupervised and semi-supervised learning methods. Methods with or without modelling. Probabilistic methods. Intelligent Agents.\nIntroduction to artificial intelligence methods without modeling (stateless - Supervised learning). The structure of the simple preceptor, neuron. The structure of neural networks. The method of backpropagation. Deep learning neural networks. Methods of nonlinear categorization.\nIntroduction to model-free artificial intelligence methods (stateless - Unsupervised learning). Classification methods, k-means, DBSCAN, spectral clustering. Unsupervised learning methods using training ( autoencoders, stacked autoencoders, deep learning).\nIntroduction to artificial intelligence methods with modeling (state modeling - deterministic). Introduction to Search Problem Modeling. Search trees. Euphemistic Methods. Local Search Algorithms and Optimization Methods. Search by width, depth. Uniform Cost Search. A Star A Star Relaxations.\nIntroduction to artificial intelligence methods with modeling (state modeling - competitive). Competitive Methods. Game Theory, Max Min Algorithms, ExpectMax Algorithms, Alpha-Beta pruning. Adversarial Generative Networks (GANs) and deep learning" . . "Presential"@en . "FALSE" . . "Geographic knowledge representation"@en . . "7.5" . "Introduction to Knowledge Representation\nConceptual structures (Semantic Networks, Lattices)\nFormal Concept Analysis (FCA)\nInformation Flow (Channel theory - Information Flow)\nConceptual Graphs (Conceptual Graphs)\nOntologies - development, extension and integration\nSemantic Similarity Measures and mapping\nNatural Language Processing (NLP)" . . "Presential"@en . "FALSE" . . "Applications of geostatistics in geological sciences"@en . . "7.5" . "Bivariate or multivariate statistics: two-dimensional distributions, conditional averages, covariance and correlation, random variables as vectors, Hilbert space of random variables.\nINTRODUCTION TO RANDOM FIELD THEORY: joint distribution of many random variables, the vector space of random variables, Covariance function and Positively Defined Functions, covariance models, simplified Random Fields.\nSTRUCTUAL ANALYSIS: Analysis of the concept of spatial correlation, definition of the barogram and its physical meaning, calculation and interpretation of the barogram, models of barograms, the barogram as a generalized covariance.\nSpatio-temporal mapping: the projection algorithm and its geometric interpretation, simple and regular kriging, cases of algorithm failure and their treatment, trend treatment, uncertain data, spatio-temporal interpolation, error estimation.\nAPPLICATIONS IN MINING: Density optimization of exploratory drilling canvass density, mineral reserve estimation, estimation error maps.\nAPPLICATIONS IN HYDROGEOLOGY - GEOMETRY: Combined use of causal and stochastic models for the interpolation of hydrogeological measurements, spatial correlation of geochemical parameters.\nAPPLICATIONS IN GEOTHERMOLOGY: Combined use of causal and stochastic models for the description of geothermal reservoirs\nAPPLICATIONS IN SOIL - ROCK ENGINEERING: Spatial distribution of soil - rock mass parameters, correlation of parameters, combined use of causal and stochastic models.\nAPPLICATIONS TO ENVIRONMENTAL POLLUTION EVALUATION: Spatio-temporal pollution mapping, correlation of pollutant distributions.\nAPPLICATIONS TO EPIDEMIOLOGY: Combined use of causal and stochastic epidemiological models to visualise the spread of public health and safety parameters." . . "Presential"@en . "FALSE" . . "Applications of geoinformatics in mining"@en . . "7.5" . "A course that includes (a) a series of introductory lectures on the principles governing the sustainable use of Mineral Resources, (b) individual/team work on the above topics. For the preparation of the assignment, which is the deliverable of the course, students proceed to the collection, organization, visualization and management of geological, demographic, spatial, environmental and other data concerning the siting of new or expansion of existing mining projects in areas of deposit interest, taking into account the particular environmental, cultural and social characteristics, and the infrastructure of the immediate and wider area under consideration.\nApplication of techniques for the analysis and spatial visualisation of these data for the rational operational planning and management of mining projects, the harmonious integration of mining activity in areas with potential for O.P.S. The above tools are also used for the examination and selection of the best alternatives for the restoration and enhancement of areas where the life cycle of mining projects has been completed and post closure activities are planned." . . "Presential"@en . "FALSE" . . "Basics of geoinformatics"@en . . "5" . "Development of the ability to recognize, identify and understand the spatial and spatio-temporal components of the reality.\nUnderstand the role of geodesy, geoinformatics and spatial data in modern world, demonstrate competences in measuring systems, methods and technologies of measurement and spatial data collection.\nDetermine and interpret the size, properties and relations of objects in space on the basis of measured data, spatial databases, plans and maps.\nRecognise problems and tasks in the application of geodetic and geoinformation principles and methods, and select proper procedures for their solution.\nKeep pace with and adopt new technological achievements in the field of surveying, geoinformation systems and services based on the position, and the changes in regulations, norms and standards. 1. Formulate the basic concepts and definitions about the space, time, space-time and reality.\n2. Explain the process of creating a model using the perceived reality, the conceptual data model and specifications (perception of reality).\n3. Explain the concept of abstract universe and discern and share the reality of the elements (entities).\n4. Describe and explain various forms of representations of the basic entities of reality.\n5. Describe the different views of spatial phenomena and connect the similarities and differences of space and time.\n6. Define the representation scale of geospace and explain its importance.\n7. Explain and describe the coordinate systems and the location of objects using an attribute.\n8. Distinguish and compare different types of maps.\n9. Explain the view of geospace based on location, object and time.\n10. Distinguish between absolute and relative spatial relationships and explain the basic idea of topological relations" . . "Presential"@en . "TRUE" . . "Spatial analysis and modeling"@en . . "4" . "THis area of Geoinformatics builds 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 geo-\nmetrical) 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\non 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." . . "Presential"@en . "TRUE" . . "Scripting languages in geodesy and geoinformatics"@en . . "3" . "Students will acquire theoretical background and practical usage of scripting languages used in geodesy and geoinfromatics in order to automate data processing in CAD and GIS applications \ndifferentiate scripting and another programming languages,\napply the programming methodology in scripting languages, \nautomatize processing of text files, spreadsheets and CAD drawings using scripting languages\nanalyze applicability and the quality of solutions in comparison to non-scripting languages,\nintegrate network geoinformation services and automatize processing of geospatial datasets using scripting programming\nlanguages." . . "Presential"@en . "FALSE" . . "Management in geodesy and geoinformatics"@en . . "3" . "The course is aimed at today’s students and tomorrow’s managers who want to understand the essentials of management as they apply within the contemporary work environment of geodesy and geoinformatics bearing in mind the context of harmonizing the Croatian business and legal environment with those in the European Union. \n Understand which personal competences are needed for managerial success.\nTo acquire a personal perspective on four basic management functions or responsibilities: planning, organizing, leading and controlling.\nIdentify different levels and types of managers in geodetic and geoinformatic company and institution.\nUnderstand fundamentals of organizing in geodesy and geoinformatics as an essential managerial responsibility.\nTo use different management structures in geodesy and geoinformatics depending on conditions such as environment, technology and size.\nTo anticipate future needs and managerial responses in geodesy and geoinformatics.\nUnderstand managerial agendas and networks in geodesy and geoinformatics.\nDemonstrate competence in understanding the management functions across cultures.\nAnalyse managers as decision makers and problem solvers in geodesy and geoinformticsTo use leading through motivation in geodesy and geoinformatics.\nDemonstrate competence in making comparative study of how management in geodesy and geoinformatics is practiced in\nCroatia and aroud the world.\nUnderstand steps in the team-building process.\nAnalyse and interpret characteristics of high-performance and poor-performance teams.\nUse team-building as an ongoing leadership responsibility.\nDemonstrate competence in distinguishing useful team roles.\nDemonstrate competence in using critical thinking in team work.\nDescribe and analyse characteristics of team members.\nIdentify skills and types of contribution which may be expected by individual team members.\nDifferentiate high performance teams as they can be applied in various fields of geodesy and geoinformatics.\nDemonstrate competence in using evaluation research to make sure that actual performance meets or surpasses company/institution objectives.\nAnalyse and interpret phases of evaluation research: 1. needs estimation, 2. program planning, 3. formative evaluation, 4. summative evaluation.\nAnalyse and interpret a concept of the „learning organization“ in geodesy and geoinformatics.\nUnderstand the process of harmonizing the Croatian business and legal environment with those in the European Union, and with the international standards of doing business." . . "Presential"@en . "FALSE" . . "Three dimensional laser scanning in geodesy and geoinformatics"@en . . "3" . "Theoretical and practical knowledge of basic spatial data collection methods using lasers practiced in geodesy and geoinformatics. \n Knowing the basis of laser technology and describing the types of laser systems\nDefining accuracy and precision of different LiDAR systems and explaining sources of errors when measuring using laser scanners\nMastering the use of terrestrial laser scanners\nApplying methods of point cloud georeferencing and registration\nUtilizing spatial data collected using terrestrial laser scanning for visualisation purposes \n Utilizing spatial data collected using space and airborne laser scanning for digital terrain model, surface and digital\nrelief model" . . "Presential"@en . "FALSE" . . "Spatial data analysis"@en . . "6" . "The course gives an overview of the technics of spatial data analysis in contemporary geoinformatics and includes several hands-on exercises. The course provides students with the skills necessary to investigate spatial patterns of social and environmental processes.\nIf the student has not taken any prerequisite courses but still wishes to participate in the course then please contact the lecturer.\n\nOutcome:\nA successful student has a systematic overview of the main ideas of spatial data analysis and main methods. Student has practical experience of solving spatial analysis tasks by means of the common GIS software." . . "Hybrid"@en . "TRUE" . . "Geo-application development"@en . . "6" . "articipants in this module will gain a well-structured understanding of\nsoftware development from a software engineering (SWE) perspec-\ntive, enabling them to work as geospatial experts in development\nteams and to successfully communicate with software developers.\nBased on the foundations of programming and development, students\nacquire competences in at least two development environments and\nlanguages, enabling them to design simple software programs, to cus-\ntomize existing applications, and to automate basic workflows. This in-\ncludes practical skills in geo-application development in the areas of\nweb applications, mobile applications, or desktop analytical applica-\ntions. Having completed this module, students are able to carry out\nbasic development tasks on a variety of platforms and architectures\nwith an emphasis on understanding and translating demands from typ-\nical geospatial application domains. This key competence is devel-\noped and verified through a development project in one of the se-\nlected IPs." . . "Presential"@en . "TRUE" . . "Geospatial fundamentals"@en . . "10" . "An introduction to the essential mathematical background of Geomatics, including co-ordinate systems, datums, map projections and co-ordinate transformations. Geospatial information acquisition, processing and management requires familiarity with certain concepts including: coordinate reference systems, datums and map projections, figures of the earth, 2&3D trigonometry, survey sampling, statistics, solving sets of equations, least squares adjustment, correlation, matrix algebra and computer programming, generally conducted within the frameworks established by national and international professional and standards organisations. It is the intention of this course to ensure that all programme participants have familiarity with these topics and having completed this, will be able to apply this knowledge in the gathering, processing and managing of geospatial data.\n\nOutcome:\nBy the end of this course students will be able to:\r\n\r\n■ Describe the basic principles of geodesy, including the size and shape of the Earth and its model, geodetic datums, map projections and coordinate reference systems;\r\n\r\n■ Explain the principles, concepts and application of coordinate datum transformations and map projection conversions including selecting appropriate map projections;\r\n\r\n■ Explain the principles and applications of least squares in surveying & mapping, using the central tendency theorem in statistics to determine the precision of a set of observations, and estimate their accuracy, and be able to determine the correlation between two sets of apparently independent variables.\r\n\r\n■ Demonstrate mathematical ability in trigonometry, statistics, calculus and linear algebra.\r\n\r\n■ Develop programming skills." . . "Presential"@en . "TRUE" . . "Geomatics and remote sensing"@en . . "6" . "Satellite missions for Earth observations are introduced: global positioning and navigation systems GNSS, optical and radar remote sensing, LASER based systems. Design and realization of control networks, real time GNSS and terrestrial LASER surveys in civil and environmental engineering application fields. Operational and methodological aspects, geometric and radiometric processing by means of geomatics techniques." . . "Presential"@en . "TRUE" . . "Geospatial science"@en . . "5" . "Description\nThe course covers generic concepts and techniques that apply to all aspects of geospatial sciences. Starting from a consideration of the nature of mapping and its uses, this also encompasses acquisition methods (including satellite and aerial imagery, laser scanning, positioning systems etc.) and fundamental concepts of reference systems. The course continues by looking at techniques for handling digital map data, and goes on to look at the work of national mapping organisations. The course is delivered through lectures, computer practicals, field trips and workshops.\n\nLearning Outcomes\n\nDevelopment of technical knowledge of the most significant techniques for acquiring spatial data, including the opportunities, challenges and limitations associated with particular techniques;\nDevelopment of appreciation and ability to critically analyse the methods, data and outputs of geospatial endeavours;\nDevelopment of fundamental knowledge of geodesy and reference systems;\nKnowledge and experience in the application of cartographic design principles in the production of mapping for the GIS coursework;\nExperience of problem- and project- definition through individual student-centred coursework brief;\nExperience of providing formative peer feedback through the end of term poster presentation;\nUnderstanding of the issues that arise in developing mapping and charting products from digital data, and the organisational frameworks in which data is acquired and utilised." . . "Presential"@en . "TRUE" . . "Geospatial fundamentals"@en . . "10" . "Short Description\nAn introduction to the essential mathematical background of Geomatics, including co-ordinate systems, datums, map projections and co-ordinate transformations. Geospatial information acquisition, processing and management requires familiarity with certain concepts including: coordinate reference systems, datums and map projections, figures of the earth, 2&3D trigonometry, survey sampling, statistics, solving sets of equations, least squares adjustment, correlation, matrix algebra and computer programming, generally conducted within the frameworks established by national and international professional and standards organisations. It is the intention of this course to ensure that all programme participants have familiarity with these topics and having completed this, will be able to apply this knowledge in the gathering, processing and managing of geospatial data.\nLearning Outcomes of Course\nBy the end of this course students will be able to:\n\n■ Describe the basic principles of geodesy, including the size and shape of the Earth and its model, geodetic datums, map projections and coordinate reference systems;\n\n■ Explain the principles, concepts and application of coordinate datum transformations and map projection conversions including selecting appropriate map projections;\n\n■ Explain the principles and applications of least squares in surveying & mapping, using the central tendency theorem in statistics to determine the precision of a set of observations, and estimate their accuracy, and be able to determine the correlation between two sets of apparently independent variables.\n\n■ Demonstrate mathematical ability in trigonometry, statistics, calculus and linear algebra.\n\n■ Develop programming skills." . . "Presential"@en . "TRUE" . . "Geomatics msc project"@en . . "30" . "Short Description\nMSc project for MSc programmes in Geomatics, Sustainable Water Environments, and Environmental Futures.\nLearning Outcomes of Course\nOutcomes will depend upon the nature of project selected, but generic outcomes are given here.\n\nOn completing this course students will be able to:\n\n■ Discuss project requirements with a client or supervisor and produce a detailed specification for the outputs of the project;\n\n■ Prepare a written project proposal and present this orally.\n\n■ Plan and carry out a substantial research task.\n\n■ Write a comprehensive report of a significant project.\n\n■ Assemble data from the project, including appropriate metadata\n\n■ Work to deadlines" . . "Presential"@en . "TRUE" . . "Python programming for geomatics"@en . . "5" . "This course gives an introduction to the Python programming language and focuses on applications for Geomatics in its assignments.\n\nAfter following this course, the student should be able to:\n1. explain and use the basic elements of a programming language;\n2. describe and give examples of some Object Oriented programming features;\n3. translate a (simple) Geomatics related problem into an algorithm;\n4. construct a correctly functioning program used in the Geomatics domain;\n5. understand the difference between an interpreted and compiled language and explain when to use one or the other." . . "Presential"@en . "TRUE" . . "Ditigal terrain modelling"@en . . "5" . "The course provides an overview of the fundamentals of digital terrain modelling:\n\n- different representations of terrains: TINs, rasters, point clouds, contour lines\n- reconstruction of terrains from different sources (lidar, InSAR, photogrammetry, multibeam echosounders)\n- spatial interpolation methods\n- conversion between different representations\n- processing of terrains and point clouds: outlier detection, filtering, segmentation, and identification and classification of objects\n- techniques to handle and process massive datasets\n- applications, eg runoff modelling, watersheds computations, visibility\n\n \nAt the end of the course, students will be able to:\n\n- describe the pros and cons of different representations of terrains\n- explain how elevation datasets can be automatically converted to terrains\n- reconstruct and manipulate terrains using with open-source libraries\n- analyse how terrains can be useful in different applications related to built environment\n- given a specific problem where elevation plays a role (eg visibility or flood modelling), analyse and identify which data and algorithms are needed to solve the problem, and assess the consequences of these choices" . . "Presential"@en . "TRUE" . . "Geomatics in practice"@en . . "10" . "The Geomatics in Practice Elective provides an opportunity to apply knowledge and skills obtained in the first year of Geomatics for the Built Environment in practice. The Internship must have an academic character.\n\ngoal: \n\t\r\nTo experience working in practice, in a company, at a (local) governmental institute or a research institute.\r\nTo apply knowledge from the Geomatics Programme in a practical project." . . "Presential"@en . "FALSE" . . "Geomatics as support for energy applications"@en . . "10" . "The course will focus on the use of 3D city models, based on the international standard CityGML, as support for energy-related applications in the framework of the energy transition. A non-exhaustive list of possible applications is:\n- Bottom-up approaches for estimation of energy performance of buildings\n- Coupling of 3D city models with specific simulation tools\n- Assessment of photovoltaic potential at urban scale\n- Integration with supply networks (e.g. gas, district heating, etc.)\n- Data modelling, definition and testing of (energy-related) data standards.\n\nThe course has both a theoretical and a practical part. Every year, a specific topic will be selected and treated during the course. Every year, depending on the selected topic, the necessary theoretical background will be provided during lectures.\n\nAfter the course the student will be able to:\n\n1) Understand the fundamental requirements for urban energy modelling\n2) Perform data requirement analysis for the modelled phenomenon starting from (but not limited to) a semantic 3D city model based on CityGML\n3) Use (and, if needed, adapt) software tools to generate, store and visualise 3D city models\n4) Depending on the specific application, implement the required procedures or, alternatively, define a proper interface between the 3D city model and the simulation tool\n5) Apply the acquired knowledge to set up and run a proper simulation environment to solve a specific problem\n6) Gather and analyse the simulation results, and possibly make them available for further applications." . . "Presential"@en . "FALSE" . . "Advanced gis for geoscientists"@en . . "7.50" . "By the end of the course, the student will have acquired:\nAn advanced skill level in performing a spatial analysis with a large GIS: being able to input data, perform GIS analyses and present results.\nA theoretical background on GIS.\nA view on GIS practice within and outside the University.\nA working experience with large and small scale spatial data and being able to apply that in research.\nPresentation of a GIS research: report, oral and poster.\nIn this “hands on” course the emphasis lays on working with GIS together with a theoretical embedding. The Software used is ArcGIS 10 (desktop) and ArcGIS PRO together with Erdas Imagine (eATE, Virtual GIS and Stereo Analyst) and Agisoft Photoscan.\n- Learning advanced theory of geospatial data analysis\n- Performing a complete GIS-analysis: datainput - analysis - dataoutput/mapmaking – scientific reporting\n- Getting familiar with DEM extraction methods\n- Training in oral and written presentation of the individual exercises\n- Training in designing and developing a poster on DEM extraction.\n- Getting familiar with current Geo-spatial-datasets\n- Getting familiar with current GIS practice. Content\nPlease note: maximum capacity for this course is 40 students. \nPriority will be given to Earth Surface and Water students, track Geohazards and earth observation.\n\nIn this “hands on” course the emphasis lays on working with GIS together with a theoretical embedding. The Software used is ESRI ArcGIS (both desktop and workstation) together with Erdas Imagine (LPS eATE, Virtual GIS and Stereo Analyst, Agisoft photoscan).\n\nThe course exists of two major parts:\nThe assignment is a traditional workflow existing of the making of a “Potential Erosion Map” of a part of South Limburg, the Netherlands. Part of the data is available (Top10 and contour lines) in digital form. Another part must be digitized (soil map). The analyses are calculating derivatives and combining the data to one or more resulting maps. The maps must be presented through hardcopies in a scientific report.\nDEM extraction of aerial photography. This project must be presented in a poster and oral presentation.\nAdditional smaller assignments can be given and must be handed in.\n\nStudents work in groups of 2 or alone if seats or software licenses allow.\nLearning advanced theory of geospatial data analysis.\nPerforming a complete GIS-project: datainput -> analysis -> mapmaking/report.\nGetting familiar with DEM extraction methods.\nTraining in oral and written presentation of the individual exercises.\nTraining in designing and developing a poster on DEM extraction\n.\nDevelopment of Transferable Skills\nHandson training GIS.\nReport writing.\nOral presentation, presentation will be video recorded.\nGiving feedback on oral presentations and posters.\nPoster making: A0 scientific poster.\nTechnical skills: using the computer programmes ESRI platform, ErdasImagine. Agisoft, introduction python." . . "Presential"@en . "TRUE" . . "Spatial analysis"@en . . "9.00" . "Objectives and Contextualisation\nAt the end of the course, the student will be able to:\n\nDominate at the practical level the different tools related to the interpolation and terrain analysis.\nUse the main applications for the generation of new information from GIS data.\nIdentify the concepts associated with spatial analysis, its applications and its limitations.\n\n\nContent\nANALYSIS IN GIS\n\n1. General Concepts of GIS Analysis\n 1.1 Introduction\n 1.2 Specifications regarding the data model\n 1.3 Combining raster-vector analysis\n\n2. Layers combinations\n 2.1 Variants and possibilities\n 2.2 Vector overlay\n 2.3 Transfer of attributes\n 2.4 Categorical data\n\n3. Map algebra\n 3.1 Previous conditions\n 3.2 Characteristics\n 3.3 NODATA\n 3.4 Multicriteria decision analysis\n\n4. Propagation of errors\n 4.1 Geometric quality criteria\n 4.2 Thematic quality criteria\n 4.3 Elimination of results by criteria of geographical insignificance\n\n5. Analysis of the landscape\n 5.1 Introduction to the conceptual and methodological framework of landscape ecology\n 5.2 Calculation and analysis of landscape indexes at various scales\n 5.3 Analysis of the ecological connectivity of the landscape\n\n6. Spatial autocorrelation\n 6.1 Concepts\n 6.2. Indicators and indexs.\n\n7. Space interpolation\n 6.1 Concepts\n 6.2 Polygons of Thiessen\n 6.3 Trend surfaces\n 6.4 Kriging\n\n8. Logistic regression\n 8.1 Characteristics\n 8.2 Spatial applications\n 8.3 Limitations and adjustments of models\n\n9. Analysis of distances\n 9.1 Cartesian distances and geodesic distances\n 9.2 Generation of continuous and buffer maps\n 9.3 Anisotropic distances and cost analysis\n 9.4 Introduction to network analysis\n\nDIGITAL TERRAIN MODELS\n\n1. Concepts\n 1.1 Fundamental concepts and terminology (DTM, DEM, DSM, etc.)\n 1.2 Models of data: raster, TIN, isolines, etc. BIM and LoD#.\n 1.3 Vertical and geoid duck\n2. Collection ofdata. Primary (field, photogrammetry, lidar, InSAR, etc.) and Secondary\n3. Generation of DTM\n 3.1 Interpolation from points: Inverse of the weighted distance (IDW), splines, kriging. Neighborhood Statistics. Processing of lidar data\n 3.2 Interpolation from isolines\n 3.3 Generation of TIN models\n4. Quality of MDT\n 4.1 Altimeter quality\n 4.2 Control of the error in the DTM\n 4.3 Propagation of error in derivative models\n5. Derived models\n 5.1 Slope, orientations, curvatures, etc.\n 5.2 Hydrographic basins, drainage network\n 5.3 Illumination, shading and solar radiation\n6. Applications\n 6.1 Applications in the processing of remote sensing images: geometric and radiometric image rectification.\n 6.2 Topographical profiles and visibility analysis\n 6.3 Three-dimensional perspectives\n \nINTERFEROMETRY\n\n1. Introduction\n 1.1. Image sensors\n 1.2. Spectral window\n 1.3. SAR missions (Synthetic Aperture Radar)\n2. SAR concept\n 2.1. Classical radar\n 2.2. SAR technical concepts\n3. SAR image\n 3.1. Spectral and reflection characteristics\n 3.2. Geometric distortions\n 3.3. Georeferencing on SAR imagery\n4. SAR Interferometry (InSAR)\n 4.1. Basic formulation\n 4.2. Coherence and noise sources\n 4.3. How to create an Elevation Map using InSAR\n5. Differential Interferometry SAR (DInSAR)\n 5.1. Basic formulation\n 5.2. How to create a ground motion map with DInSAR\n 5.3. Characteristics of DInSARproducts\n6.PSI Techniques (Persistent Scatterer Interferometry)\n 6.1. Basic concepts\n 6.2. PSI Processor Components\n 6.3. Scatterers\n 6.4. Examples of ground motion measurements with PSI\n\nCompetences\nAnalyse and exploit geographic data from different sources to generate new information from pre-existing data.\nCommunicate and justify conclusions clearly and unambiguously to both specialist and non-specialist audiences.\nContinue the learning process, to a large extent autonomously.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nIntegrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.\nUse different specialised GIS and remote sensing software, and other related software.\nUse different techniques and concepts for generating useful information in spatial analysis.\nWrite up and publicly present work done individually or in a team in a scientific, professional context.\nLearning Outcomes\nCommunicate and justify conclusions clearly and unambiguously to both specialist and non-specialist audiences.\nContinue the learning process, to a large extent autonomously.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nExploit geographic data through map algebra, layer combination, network analysis and other techniques, taking the right decisions for each problem area based on the knowledge acquired.\nIdentify the concepts associated with spatial analysis, their applications and their limitations.\nIntegrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.\nShow expertise in using the different tools of terrain analysis and interpolation.\nUse the main applications for generating new information from GIS data.\nWrite up and publicly present work done individually or in a team in a scientific, professional context." . . "Presential"@en . "TRUE" . . "Advanced gis"@en . . "9.00" . "Objectives and Contextualisation\nThis optional module, increases the knowledge acquired in the spatial analysis module of the same master, focusing on the exploitation of geographic databases from the SQL language, as well as in specific practical cases. In addition, it adds concepts associated with the publication of cartography on the Internet taking into account international standards for data and metadata that allow interoperability including semantic, technologic, information, etc.\n\nAt the end of the course, the student will be able to:\n\n1. Use different cartography publication tools on the Internet.\n2. Know the advantages and limitations of the use of standards in the GIS world.\n3. Apply international standards to the edition and publication of data and metadata on the Internet.\n4. Master queries in databases using SQL language.\n5. Design appropriately information systems for the use of data in a scientific, professional or informative context.\n\nContent\nRELATIONAL DATABASES. SQL\n\n1. Introduction to relational databases\n2. Conceptual design of a relational database: entity-relationship model\n 2.1 Foundations of relational databases\n 2.2 Entities, attributes, instances\n 2.3 Primary keys and foreign keys\n 2.4 Types of relationships and classification\n 2.5 Three-valued logic\n3. Logical design of a database\n4. The sample database: IEFC_Garrotxa.mdb\n 4.1 The Ecological and Forest Inventory of Catalonia (IEFC de la Garrotxa)\n5. Conceptual, logical and physical model of the IEFC database\n6. Physical design of a database (standardization)\n7. Practical example of designing a database for a Library\n8. Advantages of a relational database: integrity of entities and referential integrity\n9. Features of a Database Management System (SGDB)\n10. Introduction to the SQL language (management of a BD)\n 10.1 What is SQL?\n 10.2 Advantages of SQL\n11. Introduction to DML (Data Management Language) and DDL (Data Definition Language)\n12. Recovery of data with SQL: SELECT statement\n13. Simple inquiries (SELECT ... FROM)\n 13.1 Management practices with SQL databases (1)\n14. Union consultations (UNION)\n 14.1 Database management practices with SQL (2)\n15. Multi-table questions: compositions\n 15.1 Multi-table queries in SQL1\n 15.2 Internal compositions (INNER JOIN)\n 15.3 External compositions (LEFT, RIGHT and OUTER JOIN)\n 15.4 Self-composition\n 15.5 Management practices with SQL databases (3)\n16. Summary queries\n 16.1 Column functions (GROUP BY)\n 16.2 Conditions in summary queries (HAVING)\n 16.3 Database management practices with SQL (4)\n17. Subqueries\n 17.1 Comparison test with subquery\n 17.2 Proof of belonging to a subset of a subquery\n 17.3 Test of existence\n 17.4 Quantitative comparison test\n 17.5 Database management practices with SQL (5)\n18. Nested consultations\n 18.1 Database management practices with SQL (6)\n19. ODBC Link of a GIS layer with a SQL query (DSN file)\n 19.1 Creation of a DSN file for the database\n 19.2 Creation of a layer of points from the database\n 19.3 Creation of a link via ODBC of a SQL query with MiraMon's point layer\n 19.4 Creation of a link via ODBC from a SQL query with a layer of MiraMon polygons\n20. Transactions (COMMIT ROLLBACK)\n21. Update records:\n 21.1 Insertion of records (INSERT)\n 21.2 Delete and delete with subquery (DELETE)\n 21.3 Modification and modification with subquery (UPDATE)\n 21.4 Database management practices with SQL (7)\n22. DDL (Data Definition Language)\n 22.1 Definition and creation of databases\n 22.2 Definition of tables and views\n 22.3 Definition of fields\n 22.4 Definition of restrictions\n 22.5 Definition of indexes\n 22.6 Changes in the structure of the database\n 22.7 Database management practices with SQL (8)\n \nCASE STUDIES IN GIS IMPLEMENTATIONS\n\nContents based on a series of conferences by representatives of different public and/or private organizations that explain the design and use of the GIS in their work environments\n \nSTANDARDS FOR DISTRIBUTED GEOSERVICES\n\n1. Introduction\n 1.1 Interoperability and IDES\n 1.2 Standardization organizations\n 1.3 UML and XML\nExercise 1: Introduction to XML and XML Schema (Enterprise Architech XMLValidator Buddy)\n\n2. Metadata standards\n 2.1 Introduction\n 2.2 Dublin Core\n 2.3FGDC\n 2.4 ISO (19115, 19139)\n 2.5 IDEC profile\n 2.6 Profile NEM\n 2.7 INSPIRE profile\n 2.8 Metadata management applications\nExercise 2: Metadata documentation\n\n 3. Format standards\n 3.1 Modeling of data: UML and GML\nExercise 3: Introduction to GML, generation schemes from UML\n 3.2 Other format standards (SHP, MMZx, KML, GeoJSON, SWE Common, WaterML, ...)\nExercise 4: Google Earth, Google Maps and KML\n\n 4. GEOSERVICES STANDARDS\n 4.1 Catalog services: CSW\n 4.2 Display services: WMS, WMTS, OWS Context\n 4.3 Download service: WCS, WFS, SOS\n 4.4 Processing service: WPS\nExercise 5: Connection with external WMS servers.\n\nPUBLISHING CARTOGRAPHY ON THE INTERNET\n\n1. Introduction\n 1.1 Protocols\n 1.1.1 Layers of protocol\n 1.1.2 Client server architecture\n 1.1.3 Most commonly used protocol layers\n 1.2 Technological evolution of distributed GIS\n 1.2.1 Static maps (theory for exercise 0)\n 1.2.2 Static webpages (theory for exercise 1)\n 1.2.3 Interactive web maps (theory for exercise 2)\n 1.2.3.a Accelerated JavaScript and JSON\n 1.2.4 Geoservices distributed\n 1.3 Nearby technological examples\n\n2. ISO and OGC standards\n 2.1 Introduction to WxS or OWS\n 2.2 Services for the evaluation of information\n 2.2.1 Review of the Web Map Service (theory for exercises 3, 4, 5)\n 2.2.2 Use of several WMS clients\n 2.3 Services in the cloud (exercise 6)\n\n3. Practice\n 3.1 Introduction to IIS\n 3.2 Static map publication\n 3.3 Dynamic map publication\n\nCompetences\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nDesign and apply solutions based on GIS tools for managing and exploiting natural resources or administrative information with a spatial component.\nHandle different data and metadata formats appropriately and take the importance of international standards into account when storing them and publishing them on internet.\nTake a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context.\nUse different specialised GIS and remote sensing software, and other related software.\nLearning Outcomes\nApply international standards for editing and publishing data and metadata on internet.\nDesign and apply a methodology, based on the knowledge acquired, for studying a particular use case.\nDesign suitable information systems for handling data in scientific, professional or general-interest contexts.\nHandle different tools for publishing cartography on internet.\nKnow the advantages and limitations of the use of standards in the GIS field.\nShow expertise in querying databases using the SQL language.\nTake a holistic approach to problems, offering innovative solutions and taking appropriate decisions based on knowledge and judgement.\nUse acquired knowledge as a basis for originality in the application of ideas, often in a research context." . . "Presential"@en . "FALSE" . . "Spatial analysis and modelling in gis"@en . . "no data" . "no data" . . "Presential"@en . "TRUE" . . "Optional geostatistics"@en . . "no data" . "no data" . . "Presential"@en . "FALSE" . . "Geoinformatics"@en . . "5" . "Principles of spatial data quality (terminology in the\nfield of spatial data quality, the importance of data\nquality and standardization, standardized data\nquality models, elements of data quality; data\nquality control in GIS);\nOfficial spatial data sets, voluntary geographic data\nand information collection;\nIntegration of spatial data sets, process models for\ntransformations between different data formats;interoperability, INSPIRE directive, spatial data\ninfrastructure, semantic integration of spatial data;\nopen GIS;\nSpatial data for decision-making, methods of multi-\ncriteria decision-making in GIS;\nInternet and web-GIS, their relation to GIS\ntechnology, web communication and spatial data\ntransfer, web GIS;\nMobile GIS and spatial data handling in the field;\nfield computers, wireless data transfer and\ncommunication;\nCost and benefit analysis and its application in the\ndomain of geoinformation, value chain of spatial\n(geographic) data;\nVector and raster data models for graphical\npresentation of spatial data, 3D- and 4D spatial data\nmodels, advantages and weakness; importance and\ndefinition of topological rules, visualization;\nArchiving of spatial data and spatial data backups;\noptimization of GIS procedures, modelling of data\nschemes, data migrations protocols, automation of\nGIS analyses . Intended learning outcomes: Understanding of the spatial data domain and\nadvanced theoretical approaches and technological\nprocesses in the field of geoinformation;\nUnderstanding of the characteristics, strengths and\nweaknesses of existing data models and data\nprocessing methods for a given application domain;\nUnderstanding of advanced geoinformatics solutions\nand capacity of their suitable use for the selected\npurposes" . . "Presential"@en . "TRUE" . . "Spatial data analyses"@en . . "5" . "LO: Understanding of the spatial data analysis domain\nTeam and individual work experiences" . . "Presential"@en . "TRUE" . . "Geoinformatics"@en . . "5" . "no data" . . "Presential"@en . "TRUE" . . "Practicum from spatial planning"@en . . "5" . "LO: understanding the significance and the role of\nthe surveyor in elaboration of spatial documents\nand transfer of spatial planning elements to the\nspecific area\n• use of acquired knowledge for operational work\nin an the interdisciplinary team of experts in\nspatial planning ,\n• students get accustomed to connecting a wide\nrange of information related to planning, with\nan emphasis on surveying activities in spatial\nplanning" . . "Presential"@en . "TRUE" . . "Spatial statistics"@en . . "5" . "LO: Student knows and understands statistical\nmethods for the analyses of spatial data and is\nable to perform them in different problems in\nthe field of spatial planning and geoinformatics.\n• Student is able to choose the optimal statistical\nmethod according to the characteristics of the\nproblem.\n• Student understands the difference between\nstatistical analysis of non-spatial and spatial\ndata." . . "Presential"@en . "TRUE" . . "Geoinformatics"@en . . "4" . "LO: understanding selected theoretical approaches\n• understanding of the spatial data domain\n• understanding the technological processes in\nthe field of geoinformation\n• team and individual work experiences" . . "Presential"@en . "FALSE" . . "Geostatistics-geomathematics"@en . . "no data" . "no data" . . "Presential"@en . "TRUE" . . "Geoinformatics"@en . . "no data" . "no data" . . "Presential"@en . "TRUE" . . "Automatizing spatial analysis"@en . . "no data" . "no data" . . "Presential"@en . "FALSE" . . "Gis in r"@en . . "no data" . "no data" . . "Presential"@en . "FALSE" . . "Specific gis software solutions"@en . . "no data" . "no data" . . "Presential"@en . "FALSE" . . "Development of scripts and plugins in geoinformatics software (qgis, arcgis)"@en . . "no data" . "no data" . . "Presential"@en . "FALSE" . . "Higher mathematics for bachelors of geoinformatics"@en . . "4" . "The aim of the course is to create an idea of the application of mathematical methods in earth sciences and allow creative application of mathematics in the specialty, and facilitate studies in other study-related and multidisciplinary sciences that use mathematical semantics and students' logical and abstract thinking. The objectives of the course are to promote basic knowledge in certain branches of mathematics, which can be used to mathematically correctly describe and solve practical problems, as well as to acquire basic skills in solving various standard mathematics problems.\nThe course will be taught in English and Latvian.\nKnowledge 1. understands the elements of set theory: operations with sets, set equality and equivalence, basic concepts of mathematical logic: construction of expressions, their truth’s values, logical operations, denial of expression, as well as proof of the contrary; 2. understands the methodology of solving systems of linear equations, operations with determinants and matrices, their applications in solving systems of linear equations; 3. understands the definition of a function, elementary functions and their properties, the definition of a function limit, properties and calculation techniques, the continuity of a function, the definition of derivatives and differentials, their calculation techniques and applications in extreme problems and approximate calculations. 4. understand the definition and calculation techniques of indefinite integral and definite integral, as well as applications; 5. understands the definition of binary function, the definitions of their partial derivatives and differentials, calculation techniques and their applications in extreme problems and approximations, definition of double integral, calculation techniques and applications; 6. understands the concept of scalar field, examples, curves of levels, surfaces of levels, derivative in a given direction and scalar field gradient definitions, their properties and applications, vector field definition and examples. Skills 7. independently solve standard tasks on operations with sets, set equality and equivalence, determine the truth values of the expressions by performing logical operations with the expressions, constructing the denial of the expressions, as well as perform proof of the contrary; 8. independently solve standard tasks on systems of linear equations, determinants and matrices and their applications in solving systems of linear equations; 9. independently solves standard tasks on determination of elementary function definition set and value set, property research, limit calculation, continuity research, derivatives, differentials and their applications in finding extremes and approximate calculations; 10. independently solves standard tasks regarding finding an indefinite integral, calculation of a definite integral and applications; 11. independently solves standard tasks on partial derivatives of binary functions, differentials and their applications in extreme problems and approximations, double integrals and their applications; 12. independently solves standard tasks regarding scalar field level curves, level surfaces, derivatives in a given direction, scalar field gradient and its applications. Competencies 13. independently formulates the basic results on set theory: operations with sets, set equality and equivalence, expressions, their truth values, logical operations, construction of denial of expression, as well as proof of the contrary, apply them to standard and practical problems and explain results; 14. independently formulates basic results on systems of linear equations, determinants and matrices, applies them to solve standard and practical problems and explains the obtained results; 15. independently formulate basic results on the properties of elementary functions, properties and calculation of function limits, continuity of functions, derivatives, differentials, apply them to solve standard and practical problems and explain the obtained results; 16. independently formulates the basic results regarding the indefinite integral and the definite integral, the technique of their calculation, applies them to solve standard and practical tasks and explains the obtained results; 17. independently formulates basic results on binary functions, their partial derivatives, differentials, double integrals, applies them to solve standard and practical problems and explains the obtained results; 18. independently formulates the basic results of scalar field level curves, level surfaces, derivative in a given direction, scalar field gradient, its properties, applies them to solve standard and practical problems and explains the obtained results." . . "Presential"@en . "TRUE" . . "Geoinformatics regulations"@en . . "2" . "The aim of the study course is to acquire the normative base of the field of geounformatics on which the development and operation of geoinformation infrastructure, exchange of geospatial data in Latvia and Europe are based, to learn to follow the normative acts and apply them in practice; The task of the study course: 1. To learn to work with the State Policy Planning Database POLSIS; 2. To get acquainted with the document of political formation of the sector: “Latvian geospatial information development concept”; 3. To get acquainted with the legislation of Latvia: Geospatial Information Law; 4. To get acquainted with the accompanying orders of the Geospatial Information Law and the regulations of the Cabinet of Ministers; 5. To get acquainted with the database of European Union regulatory enactments EUR-Lex; 6. Familiarize yourself with Directive 2007/2 / EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE); 7. Get acquainted with the regulations accompanying the directive; 8. To learn to orientate in the normative acts of geospatial information and their practical application. The language of instruction is Latvian. \nKnowledge: 1. Understands the regulatory enactments and standards governing the field; 2. Knows the requirements of regulatory enactments regulating the field of geoinformatics. Skills: 3. Select the regulatory enactments necessary for solving the problem; 4. Apply the regulatory enactments regulating the field of geoinformatics in their work; 5. Documents shall be drawn up in accordance with regulatory enactments in the field of record keeping and GIT. Competence: 6. Recognizes and selects the regulatory enactments necessary for solving the problem; 7. To work with the regulatory enactments regulating the industry and apply them." . . "Presential"@en . "TRUE" . . "Russian language in geoinformatics"@en . . "2" . "no data" . . "Presential"@en . "TRUE" . . "English for students of geoinformatics"@en . . "2" . "The course aims to develop students’ spoken and written English language skills to be used for academic purposes and in various areas where geoinformatics is applied. The course provides for the acquisition of the fundamental geography and GIS (geographic information system) terminology in English as well as raises students’ awareness of academic and specialized language. The enabling objectives of the course are to develop students’ skills for selecting academic literature, reading it for acquiring information and making summaries, abstracts and reports as well as selecting professional literature on similar topics to compare the specific features of the academic and professional discourse; repeat and consolidate students’ knowledge of grammar at level B2-C1; consolidate students’ skills of expressing a professional opinion in English and update students’ presentation skills. The topics covered in the course ensure ample opportunities for learning and practicing co-operation, interaction and mediation strategies as well as facilitate critical thinking and digital literacy which can be used for further development of their skills and knowledge. The course is organized in accordance with the students’ prior knowledge and acquired skills and more extensive support programme is offered to the students with insufficient skills of independent foreign language acquisition and lower level of knowledge.\nThe language of instruction is English.\nResults Knowledge: 1. Know the basic terminology in English on the topics examined in the course. 2. Know the terminology on the topic of their interest in geoinformatics from the individually studied professional articles. 3. Are aware of linguistically precise use of terms in a sentence and grammatically correct formation of questions, tenses, voice and mood. 4. Understand the key principles of reading EU directives. Skills 5. Ask questions and expressing an opinion (guiding a conversation) and reporting the acquired information by organizing the text in a logical structure. 6. Read and comprehend professional and scientific geoinformatics literature and express opinions on the studied material as well as professional topicalities. 7. Present information in English loudly, clearly and in an understandable manner. Competences: 8. Interactively use various resources (professional and language knowledge resources, scientific, popular and reference resources in English and Latvian) for the acquisition of the necessary information, summarize the acquired information and present it in English individually and in a group). 9. Cooperate within a group – agree on a topic, its rationale, engage in brainstorming; jointly select information and opinions to be presented as well as draft, correct, edit and deliver the presentation material." . . "Presential"@en . "TRUE" . . "Environmental monitoring and geomatics"@en . . "3" . "The aim of the study course is to provide knowledge about environmental monitoring systems, their operating principles, environmental data acquisition and processing approaches, as well as to ground an understanding of environmental data management, data quality, data analysis and visualization possibilities. The study course provides theoretical and practical knowledge in order to understand and practically develop full-stack environmental monitoring systems comprehending analog signal acquisition and processing in microcontroller side, large-scale data management on the server side as well as data analysis and integration with geographic information systems. The tasks of the course, which provide knowledge, skills and competencies, are: 1. To comprehend environmental monitoring systems, principles of development and methods; 2. To master the development of an environmental monitoring systems based on microcontrollers; 3. To gain skills to manage acquired data and to be able to critically evaluate the quality of the data; 4. To acquire competencies related to the practical application of the considered methods.\nLanguage of instruction: Latvian and English\nCourse responsible lecturer Zaiga Krišjāne\nResults Knowledge 1. Describe and compare environmental monitoring sensing systems, their types and operating principles, as well as application possibilities; 2. Explain the operation principles of electronics and microcontrollers and their parameters; 3. Explain the differences of common programming languages and can select the most appropriate language for a given task; 4. Outlines best basic principles of data management, data processing and quality assessment methods; Skills 5. Design and develop environmental monitoring system prototypes using a wide range of sensors and components; 6. Develop data storage and processing systems for integration with environmental monitoring systems/microcontrollers; 7. Develops full-stack environmental monitoring systems. Competence 8. Evaluate environmental monitoring systems and compares them for the implementation of specific objectives; 9. Select the most suitable microcontrollers and sensors for specific environmental monitoring objective; 10. Select the most appropriate data processing and quality assessment methods for the needs of environmental monitoring, as well as integrates the obtained data into geographical information systems" . . "Presential"@en . "FALSE" . . "Geostatistics"@en . . "4" . "Theoretical and practical issues related to geostatistics.Tobler’s Laws, spatial autocorrelation, conditions for the application of geostatistics, definitions of: regionalized variable, variogram, covariance, kriging. Selected ex\u0002amples of geostatistics, including in preparing maps of real property value." . . "Presential"@en . "FALSE" . . "Spatial planning"@en . . "2" . "1. The spatial planning system in Poland. 2. Planning documents prepared at the local level. 3. Procedures for preparing a study of the conditions and directions of spatial development and a local spatial development plan. 4. Social participation in the process of preparing planning documents. 5. The degree of detail in planning arrangements regarding, inter alia: the principles of division into building plots; lines, parameters and indicators of the building and communication service. 6. Planning situation of communes in Poland. 7. Urban and Architectural Commission. 8. Decision on building conditions. 9. Decision on the location of a public purpose investment. 10. Economic analysis of the implementation of the local spatial development plan." . . "Presential"@en . "TRUE" . . "Geospatial analysis and interpretation"@en . . "5" . "no data" . . "Presential"@en . "TRUE" . . "Methods of spatial analysis"@en . . "8" . "Mathematics for Geomatics; GIS\nStatistics and analysis of geographic data\nSpatial analysis" . . "Presential"@en . "TRUE" . . "Agent-based modelling and simulation in air transport"@en . . "4.00" . "Course Contents Introduction. Agents and Multiagent systems. Agent-based modelling architectures. Examples from air transportation.\nEmergence in Multiagent systems. Agent-based simulation. Agent-based modeling and simulation tools.\nAgent-based coordination, planning, and scheduling in air transportation.\nNature-inspired approaches to solve optimization problems. Swarm intelligence.\nAdaptive behavior and learning in agent-based systems.\nCollaborative decision making in air transportation. Negotiation, auctions, game-theoretic approaches.\nAgent-based model analysis: sensitivity, uncertainty, robustness. Validation of agent-based models.\nStudy Goals The student has to be able:\n- to formulate a practical air transportation problem as an agent or a multiagent system model;\n- to identify appropriate agent-based methods and to apply them;\n- to implement agent-based models;\n- to perform agent-based simulation, interpret and analyze simulation results;\n- to be able to apply agent-based optimization techniques" . . "Presential"@en . "TRUE" . . "Geomatics"@en . . "20" . "Geomatics is the name given to the combination of geographical information systems (GIS), remote sensing and Earth observation (EO), and spatial data acquisition, analysis and visualisation. \n \n These methods and technologies have become vitally important in environmental management, and are being increasingly used by scientists, governmental agencies, environmental NGOs and businesses. \n \n Geomatics enable us to view, question, understand, interpret, and visualize spatial datasets, and by explicitly considering space and location we can unravel underlying relationships, patterns, and trends that may not be apparent when using non-spatial analytical methods. Geomatics skills are highly sought in the job market, as they comprise a powerful suite of problem-solving and decision-making tools. \n \n You’ll be introduced to the basic concepts and principles of Geomatics, with emphasis on using Geographic Information Systems (GIS) and EO (remote sensing). You‘ll also be introduced to essential methods for acquiring and analysing spatial data from varied sources, and learn how to interpret and report the results of these analyses." . . "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" . . "Multivariate statistics | spatial statistics | geostatistics"@en . . "6" . "no data" . . "Presential"@en . "TRUE" . . "Remote sensing and gis software"@en . . "3.0" . "### Working language\n\nPortuguês - Suitable for English-speaking students\n\n### Goals\n\nIn this Curricular Unit it is intended that students acquire skills and knowledge within the scope of the computational development of new tools/applications in the areas of remote sensing and GIS. More specifically:\n\n1. Know the graphical environment of the software to be used.\n2. Use of GIS and Remote Sensing tools.\n3. Acquire training on geospatial libraries and programming paradigms involved.\n4. Automating algorithms for data processing and analysis.\n\n### Learning outcomes and skills\n\nDuring contact with the software and available tools, the student will be encouraged to know, explore and question the functioning of the software with the monitoring of the code presented in the python-_notebook_ and its implementation, contributing to the consolidation of knowledge; classes using software and script development will allow the student to finalize the creation of an application/tool, integrating all the concepts learned so far within the scope of the Curricular Unit.\n\n### Working mode\n\nIn person\n\n### Program\n\n2. Exploration of spatial data modules: GDAL/OGR.\n \n4. Introduction to QGIS software.\n * Exploitation of remote sensing oriented tools integrated in QGIS.\n * Semi automatic classification plugin.\n * Orfeo-Toolbox.\n\n * Introduction to PyQt4 library and QGIS API (qgis.gui and qgis.core).\n * Official framework for creating applications in QGIS software.\n * Importation of algorithms from the Processing Toolbox framework.\n \n6. Introduction to GRASS-GIS software (vector and raster layers; layer properties; visualization of geospatial information; geprocessing; manipulation of multispectral images).\n \n8. Introduction to processing SAR Image products with ESA-SNAP. Radiometric calibration, noise reduction with application of filters and multilook technique, geometric correction. Integration of the final product in GRASS-GIS for geospatial analysis.\n \n\n### Mandatory Bibliography\n\nLawhead, J.; QGIS Python Programming Cookbook, 2015\nMarkus Neteler, Helena Mitasova; Open Source GIS: A GRASS GIS Approach, Springer, 2010\nGary Sherman; Desktop GIS: Mapping the Planet with Open Source Tools, Pragmatic Bookshelf, 2008\n\n### Teaching methods and learning activities\n\nClasses are taught with an essentially practical component and complemented with a theoretical context that will be presented in _the form of worksheets integrated between the different software_, where students follow the material interactively and implement it using the QGIS, GRASS software -GIS and ESA-SNAP.\n\nAssessment is carried out at the end of the program with a practical exam where problems are posed and students must think/reflect and implement a solution. This assessment is carried out with the support of the interactive _notebook_ provided in class.\n\nPractical exam – 100%\n\n### Software\n\nQGIS\nGRASS-GIS\n\n### Type of evaluation\n\nEvaluation by final exam\n\n### Assessment Components\n\nExam: 100.00%\n**Total:**: 100.00%\n\n### Occupation Components\n\nFrequency of classes: 21.00 hours\nSelf-study: 60.00 hours\n\n**Total:**: 81.00 hours\n\n### Get Frequency\n\nFrequency is obtained by taking a final exam.\n\n### Final classification calculation formula\n\nFinal exam.\n\nMore information at: https://sigarra.up.pt/fcup/pt/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=479387" . . "Presential"@en . "TRUE" . . "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" . . "Agents and multi-agent systems"@en . . "6.0" . "The notion of an (intelligent) agent is fundamental to the field of artificial intelligence. Thereby an agent is viewed as a computational entity such as a software program or a robot that is situated in some environment and that to some extent is able to act autonomously in order to achieve its design objectives. The course covers important conceptual, theoretical and practical foundations of single-agent systems (where the focus is on agent-environment interaction) and multi-agent systems (where the focus is on agent-agent interaction). Both types of agent-based systems have found their way to real-world applications in a variety of domains such as e-commerce, logistics, supply chain management, telecommunication, health care, and manufacturing. Examples of topics treated in the course are agent architectures, computational autonomy, game-theoretic principles of agent-based systems, coordination mechanisms (including auctions and voting), and automated negotiation and argumentation. Other topics such as ethical or legal aspects raised by computational agency may also be covered. In the exercises and in the practical part of the course students have the opportunity to apply the covered concepts and methods.\n\nPrerequisites\nDesired Prior Knowledge: Basic knowledge and skills in programming.\n\nRecommended reading\nStuart Russell and Peter Norvig (2010). Artificial Intelligence. A Modern Approach. 3rd edition. Prentice Hall.\nGerhard Weiss (Ed.) (2013, 2nd edition): Multi-agent Systems. MIT Press.\nMike Wooldridge (2009, 2nd edition): An Introduction to Multi Agent Systems, John Wiley & Sons Ltd.\nYoav Shoham and Kevin Leyton-Brown (2009): Multi-agent Systems. Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press.\n\nMore information at: https://curriculum.maastrichtuniversity.nl/meta/462431/agents-and-multi-agent-systems" . . "Presential"@en . "FALSE" . . "Space geodesy and geomatics"@en . . "6.0" . "- Understand spatial geodesy techniques (GNNS, VLBI, SLR) for the georeferencing of spatial data and methods for multi-temporal processing of optical remote sensing data, radar and lidar.\n- Develop skills on space geodesy and satellite and aerial remote sensing techniques for the control, monitoring and prevention of natural or anthropogenic risks that involve degenerative processes on the environment and on the territory (hydrogeological instability, coastal erosion, storage pollution of waste and industrial areas, state of vegetation, etc.)\n- Understand the methods and tools for the construction of WEBGIS and georeferenced databases, from urban to territorial scale, useful for the management of goods production systems and the provision of sustainable services (e.g. control of the stability of buildings and infrastructures, maintenance of technological and transport networks, management of green areas, etc.)\n- Experience on experimental data in the thematic laboratory to be developed on real case studies" . . "Presential"@en . "TRUE" . . "Spatial analysis and modelling"@en . . "15.0" . "EGM716 – Spatial Analysis and Modelling (15 credits)\n\nThis module builds on the introductory material of EGM711 and EGM712, covering key concepts of spatial data analysis and modelling, and providing extensive practical experience of ESDA and spatial modelling within a GIS environment." . . "Presential"@en . "FALSE" . . "Research methods: geoinformatics & earth observation"@en . . "6" . "Selection of Geoinformatics or Earth Observation subfield for the master’s thesis and critical review of relevant literature. Upon completion of this course, it is expected that the learner will be able to critically review pertinent literature (Greek and English) in Geoinformatics or Earth Observation, as well as develop research questions and hypotheses." . . "Presential"@en . "TRUE" . . "Specialization: geoinformatics & earth observation"@en . . "6" . "Specialization in a subfield of Geoinformatics or Earth Observation. Upon completion of this course, it is expected that the learner will be able to: (1) critically review existing literature in a subfield of Geoinformatics or Earth Observation, (2) develop research questions and hypotheses, (3) use geoinformatics or Earth Observation in a small-project setting." . . "Presential"@en . "TRUE" . . "Integrated design for civil engineers & surveying and geoinformatics engineers I"@en . . "4" . "no data" . . "Presential"@en . "TRUE" . . "English for civil engineers & surveying and geoinformatics engineers"@en . . "4" . "LCE 120 is a three hour per week, 4-credit, required level course that concentrates on the learning of English for Specific Academic Purposes. LCE 120 is particularly designed to meet the needs of university students studying in the field of Civil Engineering. This course intends to familiarise the students with relevant reading material. This will be used to acquaint the students with genre (proposals, lab reports, memos, instruction manuals) and writing styles (cause and effect). Furthermore, learners are expected to develop their listening comprehension and speaking fluency by taking an active part in discussions, giving oral presentations, defending their opinion etc. They are expected to develop sufficient range of language, phonological control and sociolinguistic awareness to be able to express themselves with a degree of clarity, fluency and spontaneity." . . "Presential"@en . "TRUE" . . "Integrated design for civil engineers & surveying and geoinformatics engineers II"@en . . "4" . "no data" . . "Presential"@en . "TRUE" . . "Geography and spatial analysis"@en . . "4" . "This course introduces the students to the principles of human geography and the basic methods of spatial analysis and is divided into three interrelated parts. The first part examines the basic components of human geography: population, environment and resources. The second part introduces the methodological principles applied in human geography and the last part presents the most commonly used methods such as regression, factor analysis and location-allocation methods." . . "Presential"@en . "TRUE" .