. "Geographic Information Science"@en . . "Remote Sensing"@en . . "English"@en . . "Sensing technologies"@en . . "5" . "This course provides an overview on the principles and applications of spaceborne, airborne and terrestrial sensing technologies for geographic data acquisition as well as techniques for processing and information extraction from the acquired data. \n\nAfter the course the student is able to\n\n• describe different sensing techniques, from different platforms, for acquiring geospatial data\n• explain the applied metrics for quality assessment of the acquired data\n• apply data processing and machine learning techniques on the (self) acquired geographic data\n• analyse the applied data processing/ information extraction algorithms and evaluate their capabilities/drawbacks\n• compare different sensing techniques for their suitability in different application domains from their efficiency and quality points of view\n• select appropriate sensing technologies for tackling a spatial decision making problem" . . "Presential"@en . "TRUE" . . "Geographical information systems (gis) and cartography"@en . . "5" . "The course provides an overview of Geographical Information Systems (GIS) and digital Cartography, and of how GIS can be used in practice to solve real-world problems. The course also provides students with theoretical background knowledge of concepts, data types and GIS-related typical processes and algorithms of GIS packages.\n\n \nAfter the course “Introduction to GIS and digital cartography” the student will be able to:\n1) Explain what a GIS is and what real-world problems it can help solve;\n2) Describe the quality aspects of geo-datasets and compare the two conceptualisations of space (field versus objects), and how these are modelled in a GIS;\n3) Use a GIS to visualise, convert and analyse geographical datasets coming from different sources;\n4) List the main spatial data structures and algorithms used in GIS, compare and discuss them in terms of applicability depending on the problem to be solved;\n5) Explain and analyse what the basic spatial operations are and consist of, and how they are performed;\n6) Generalise the GIS knowledge to solve more complex spatial problems by integrating existing tools and\ndeveloping tailored solutions/workflows." . . "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" . . "Geodatabase management systems"@en . . "5" . "This course is about managing geo-information in a database management system (DBMS). \n\n \nAfter the course the student is able to:\n- design an conceptual information model by converting a descriptive text of a real world situation into a Unified Modelling Language (UML) class diagram.\n- create a relational database management system (DBMS) schema to store the information for a real world situation (as captured in a UML class diagram), including definition of (primary/foreign) keys, clustering and indexing for performance.\n- apply the Structured Query Language (SQL) to query and update a relational DBMS by using a range of techniques: join two or more tables, aggregate data, specify meaningful selection predicates, ordering the output, use subqueries and program additional functionality with a procedural language (PL/pgSQL in PostgreSQL).\n- understand the different characteristics of spatial data and be able to also design a conceptual model for a spatial DBMS, create the spatial conceptual information model in a RDBMS with spatial extensions, optimize the implementation of spatial databases (spatial clustering indexing, spatial constraints,...) and retrieve and update spatial data using spatial operations (both geometric and topological) in the selection in combination with non-spatial predicates.\n- apply a range of advanced topics: spatial-temporal modelling, 3D modelling, routing inside the DBMS (using pgRouting) vario- or multiscale modelling, Spatial OCL (Object Constraint Language) formalization, simplicial homology, nD point clouds, efficient raster data management, 5D modelling, and selected NoSSQL database (Neo4j, MongoDB)." . . "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" . . "Positioning and location awareness"@en . . "5" . "This course addresses Global Navigation Satellite Systems (GNSS) and (indoor) positioning technologies for sensing people, devices, and assets in the built environment with the focus on location-aware applications. The course covers the requirements and context for these location-aware applications: global, local, and linear reference systems, coordinate systems and map projections, positioning methods and techniques, and the social and technical push and legislative pull factors that empower the development of location-based services.\n\n\t\r\nAfter the course Positioning and Location Awareness the student is able to:\r\n1. Understand location awareness, location sensitivity, context awareness;\r\n2. Understand the different types of reference systems: global, local (Dutch), linear;\r\n3. Understand the ethical and legislative factors of methods for location awareness;\r\n4. Apply different coordinate systems, positioning, and indoor localisation;\r\n5. Evaluate different technologies to support location awareness on their technical performance (availability, accuracy, integrity, continuity), and their ethical factors and legislative factors (privacy issues)." . . "Presential"@en . "TRUE" . . "3d modelling of the built environment"@en . . "5" . "This course provides a detailed description of the main ways in which the built environment is modelled in three dimensions, covering material from low-level data structures for generic 3D data to high-level semantic data models for cities.\n\n \nAt the end of the course, students should be able to:\n\n1. compare different modelling approaches, outline their relative merits and drawbacks, and choose an appropriate approach for a given use case;\n2. interpret topological properties like 2-manifoldness, and execute solutions that use these properties to store 3D models;\n3. implement several different data structures for the storage of 3D models;\n4. outline the characteristics of the main semantic open data models used in GIS (CityGML-CityJSON) and BIM (IFC), and to manipulate such models at a low level;\n5. execute analyses based on a 3D city model and check their result." . . "Presential"@en . "TRUE" . . "Geo-information governance"@en . . "5" . "In this course students will learn about the organisational and legal aspects relevant for developing a strategy for a geographic information infrastructure.\n\nAfter this course the student is able to:\n- recognize and anticipate upon relevant legal and organisational issues related to the acquisition, processing, dissemination and use of (open) geo-information\n- apply the concepts, processes and main components of geo-information infrastructures to support geo-information sharing between organisations\n- critically assess geo-information management strategies for organisations\n- assess the performance of an geo-information infrastructure from a user perspective\n- (co-)author a scientific paper on a selected SDI topic" . . "Presential"@en . "TRUE" . . "Geoweb technology"@en . . "5" . "The course consists of four parts:\n\n1. Principles, application, evaluation and integration of generic web services and the importance of using standards when building them. The standards involved start with general ICT standards like HTTP, XML and JSON. Increasingly the semantics of data in the framework of web services, and linked data, plays an important role.\n\n2. Based on these general standards, geo-standards and protocols can be build (ISO TC-211/OGC web services and protocols). Combining the geo-standards and protocols results in a set of geo web services that more and more replace the traditional GIS tools. Topics of interest: geo-web system server-client architecture, WMS/WFS/... server, desktop/browser/mobile clients (incl. web application development), web-based transaction processing, portrayal, query (filter encoding), tiling, metadata service, web processing service, point cloud & vario-scale data services.\n\n3. Principles and applications of (real-time) Sensor Web. The components of Sensor Web will be explained and (some of them) used: Observations & Measurements, Transducer Markup Language, Sensor Observation Service, Sensor Planning Service, Sensor Alert Service and the Web Notification Service.\n\n4. Applying software tools for the visualization of 3D geo-data. Normally the first, and in many cases the most important, thing to do with (3D) geo-data is to visualize it. Nowadays data is delivered over the internet by means of web services. The standards, architecture and services for 3D geo-visualization, and the practical aspects of available (web) tools and how to use them will be explored.\n\n \nAfter the course the student is able to:\n\n- understand generic internet and web standards and apply these in the implementation of web information systems\n- describe the existing formal geo-standards, derived implementation specifications and related geo web services and their application domains\n- understand the Sensor Web Enablement Common Data Framework (common data models and schema) and the SensorML (models and schema for sensor systems and processes surrounding measurements) and use them in web services\n- describe the available (visualization) standards for the visualization of 3D geo-data, like X3D, 3D-pdf, WebGL, CityGML, KML, GeoJSON, glTF, b3dm, i3s and other industry standards / software tools\n- integrate several geo web services for supporting spatial decision making\n- evaluate existing geo web services in terms of specified applications and give motivated suggestions for improvement" . . "Presential"@en . "TRUE" . . "Photogrammetry and 3d computer visioin"@en . . "5" . "Photogrammetry and 3D computer vision aim at recovering the structure of real-world objects/scenes from images. This course is about the theories, methodologies, and techniques of 3D computer vision for the built environment. In the term of this course, students will learn the basic knowledge and algorithms in 3D computer vision through a series of lectures, reading materials, lab exercises, and group assignments. The topics cover the whole pipeline of reconstructing 3D models from images:\n- Cameras models: how a point from the real world gets projected onto the image plane and how to recover the camera parameters from a set of observations;\n- Epipolar geometry: the geometric relations between 3D points and their images points; the constraints between the image points;\n- Image matching: define and match image features (SIFT) to establish correspondences between images;\n- Structure from motion: recover/refine geometry and camera parameters from a set of images;\n- Multi-view stereo and learning-based approaches for recovering dense geometry (e.g., point clouds) from images;\n- Surface reconstruction: obtain 3D surface models of real-world objects from point clouds.\n\n \nAfter finishing this course, the students will be able to:\n- apply linear algebra knowledge to implement basic 3D computer vision algorithms;\n- explain the main concepts in 3D computer vision (i.e., camera models, epipolar constraints, fundamental matrix, image matching, triangulation, structure from motion, bundle adjustment, multi-view stereo, and surface reconstruction);\n- explain the principles of the state-of-the-art 3D computer vision pipelines for 3D dense reconstruction from images;\n- evaluate methods for reconstructing smooth surfaces and piecewise planar objects, and choose applicable methods to solve specific reconstruction problems;\n- propose and implement solutions for reconstructing real-world buildings from images." . . "Presential"@en . "TRUE" . . "Thesis preparation"@en . . "10" . "Preparation of Graduation Project- The graduation work is the last step in the Geomatics education. It is completed in the form of an individual research project. GEO2020 follows GEO2011, and involves carrying out the research according to the plan of GEO2011.\nOn completing the Graduation Project, the student is able to:\n1. Demonstrate that they are capable to independently apply relevant theory and/or knowledge to research;\n2. Formulate a theoretical, numerical and/or experimental framework and delineate a research problem such that it can be solved;\n3. Interpret obtained results in a critical manner;\n4. Explain the work performed in a structured report that incorporates verification of methods and tools and is written in correct English;\n5. Communicate the work performed in a structured way through an oral presentation to a wider audience;\n6. Demonstrate capacity to manage the project, both technically and time-wise, considering resources and methodology;" . . "Presential"@en . "TRUE" . . "Graduation project"@en . . "30" . "The topic of the scientific project can be related to any of the subjects offered by the Geomatics programme. The project must be supervised by one or more of the lecturers who participated in the Geomatics education. The project can be performed in cooperation with a company/institution, which provides a use case or a problem statement (but the work is always supervised by the scientific staff of the university).\r\n\r\nThe graduation work deliverables are: (1) a scientific report (a thesis); and (2) an oral presentation.\n\nDuring the MSc thesis the student will show their knowledge, understanding and skills at an academic Master’s level with respect to independently planning and executing a research project in the field of Geomatics.\r\n\r\nOn completing the Graduation Project, the student is able to:\r\n1. Demonstrate that they are capable to independently apply relevant theory and/or knowledge to research;\r\n2. Formulate a theoretical, numerical and/or experimental framework and delineate a research problem such that it can be solved;\r\n3. Interpret obtained results in a critical manner;\r\n4. Explain the work performed in a structured report that incorporates verification of methods and tools and is written in correct English;\r\n5. Communicate the work performed in a structured way through an oral presentation to a wider audience;\r\n6. Demonstrate capacity to manage the project, both technically and time-wise, considering resources and methodology;" . . "Presential"@en . "TRUE" . . "Joint interdisciplinary projects"@en . . "15" . "https://www.jointinterdisciplinaryproject.nl/" . . "Presential"@en . "FALSE" . . "Research assignment"@en . . "2" . "The course is meant as an introduction to one, or more, aspects of a research project.\n\n\t\r\nAfter the course the student is able to:\r\n- formulate a clear problem definition with research questions;\r\n- independently carry out an assignment;\r\n- document the results of the assignment." . . "Presential"@en . "FALSE" . . "Land administration"@en . . "5" . "This course gives an introduction to the field of land administration.\n\nAfter the course the student is able to:\r\n- Describe and explain the underlying legal principles, institutional arrangements, and technology of Land Administration Systems (LAS);\r\n- Assess, based on a systems approach, the practical value of a specific (national) LAS and possibly propose improvements;\r\n- Interpret the data from a LAS in a specific case, and give a reasoned opinion about the meaning and value of this information on rights, restrictions and responsibilities in this specific case;\r\n- Explain the increasing importance of novel developments such as 3D Land Administration, international standards (ISO19152, the Land Administration Domain Model), integration of land registration, valuation and spatial plan information, and support of sustainable development." . . "Presential"@en . "FALSE" . . "Modelling wind and dispersion in urban environments"@en . . "5" . "The course focuses on the modelling of winds and dispersion around 3D city models. The goal is to further the students experience in geomatics knowledge by learning tools with direct application to real urban scenarios. The course covers the necessary fundamentals of fluid dynamics and computational fluid dynamics methodologies to perform simulations in urban environments.\n\nAfter the course the student is able to:\n\n1) Understand the fundamental requirements for wind and dispersion simulations;\n2) Perform data requirement analysis for the modelled phenomenon starting from (but not limited to) a semantic 3D city model;\n3) Apply the acquired knowledge to set up and run a correct simulation environment to analyze wind and dispersion in an urban area;\n4) Gather and analyse the simulation results, and make them available for further applications." . . "Presential"@en . "FALSE" . . "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" . . "Machine learning for the built environment"@en . . "5" . "This is an introductory course for machine learning to equip students with the basic knowledge and skills for further study and research of machine learning. It introduces the theory/methods of well-established machine learning and state-of-the-art deep learning techniques for processing geospatial data (e.g., point clouds). The students will also gain hands-on experiences by applying commonly used machine learning techniques to solve practical problems through a series of lab exercises and assignments. \n\nAfter the course, the students will be able to:\r\n- explain the impact, limits, and dangers of machine learning; give use cases of machine learning for the built environment;\r\n- explain the main concepts in machine learning (e.g., regression, classification, unsupervised learning, supervised learning, overfitting, training, validation, cross-validation, learning curve);\r\n- explain the principles of well-established unsupervised and supervised machine learning techniques (e.g., clustering, linear regression, Bayesian classification, logistic regression, SVM, decision tree, random forest, and neural networks);\r\n- preprocess data (e.g., labelling, feature design, feature selection, train-test split) for applying machine learning techniques;\r\n- select and apply the appropriate machine learning method for a specific geospatial data processing task (e.g., object classification);\r\n- evaluate the performance of machine learning models." . . "Presential"@en . "FALSE" . . "Ethics for the data driven city"@en . . "5" . "This course discusses the main principles of data ethics and the relevance of applied ethics in the domain of the data-driven city to answer the main question of how to safeguard the human dimension in a data-driven world.\n\n \nAfter this course, the student is able:\n- To explain the relevance of applied ethics in relation to the design and governance of the data driven city in general\n- To identify concrete ethical issues within the field of the use of data in a concrete situation\n- To identify and apply the relevant ethical principles for this concrete situation.\n- To create a framework for the application of technology in the data-driven city and to formulate a substantiated standpoint regarding the admissibility of this technology." . . "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" . . "Master of Science Geomatics"@en . . "https://www.tudelft.nl/en/education/programmes/masters/geomatics/msc-geomatics" . "120"^^ . "Presential"@en . "With Geomatics for the Built Environment you will learn to use advanced techniques in data analysis, geographic data modelling and the visualisation of data. To be able to do this, you will develop expert skills in different programming languages such as Python, C++ and SQL, as they are essential for solving many of the Geomatics-related questions we need to consider.\r\n\r\nThe remote sensing techniques that you will learn in this programme give you the ability to measure and observe our environment and especially those features that cannot be seen with the human eye. Data management and analysis techniques allow you to turn these measurements into useful information and knowledge, with which patterns can be identified, behaviour over time can be tracked and a future state can be predicted. You will apply your skills in 3D modelling, GIS, mapping, serious gaming, simulation and visualisation to a wide range of fields such as urban planning, disaster management, geodesign, location-based services (LBS) and land administration. \r\n\r\nBeyond that, you will acquire knowledge about the use and governance of geographic data. In Geomatics you will implement the principles of open science to the greatest extent possible. In your assignments you will use open data and open source software, and your research results will be openly published in the university’s repository."@en . . . "2"@en . "FALSE" . . "Master"@en . "Thesis" . "2314.00" . "Euro"@en . "20560.00" . "Recommended" . "Geomatics graduates are specialists with a broad base who are able to position themselves across the entire breadth of the Geomatics spectrum. The Geomatics working field is expanding quickly so there is ample opportunity to find a position that suits you best or to start your own company. \n\nProspects:\n- geovernmental and private organisations (companies)\n- PhD research"@en . "no data" . "TRUE" . "Downstream"@en . . . . . . . . . . . . . . . . . . . . . . "Faculty of Architecture and the Built Environment"@en . .