Search result: Catalogue data in Autumn Semester 2023
Geomatics Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 Master Studies (Programme Regulations 2022) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Compulsory Courses | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0248-00L | Geospatial Research Methods | O | 4 credits | 4G | M. Raubal | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The goal of this seminar-style course is to convey methods how to do research and communicate research results in the geospatial domain. The course further provides an overview of the types of research in the geospatial domain and the research life cycle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will exercise important aspects when doing research, such as doing a literature search, writing and referencing, and presenting. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 103-0249-00L | Geospatial Reference Systems | O | 4 credits | 4G | A. Wieser, M. Varga | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course is an advanced introduction to spatial and temporal reference systems for acquisition, analysis and communication of geospatial data. The course covers definitions, conventions and comprehensive real world examples of coordinate reference systems, time reference systems, their respective practical realization, and operations for changing data between them. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After this course the students should be able to describe the most important established national and international spatial and temporal reference systems; describe the techniques, processes, and institutions needed to establish and maintain reference frames; select appropriate reference systems and frames for specific geospatial modeling/analysis tasks; carry out coordinate transformations, conversions, and time operations on geospatial data, taking into account and quantifying the uncertainties; combine geospatial data originally referring to different reference frames into a single reference frame. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course requires familiarity with linear algebra and analysis at the level of a BSc program in engineering or natural sciences. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0250-00L | Geospatial Data Acquisition | O | 4 credits | 4G | A. Wieser | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course supports the students in acquiring an in-depth understanding of sensors, sensor systems and sensor networks for the acquisition of geospatial data. Emphasis is put on the prediction and assurance of data quality based on an understanding of key sensing principles, external influences, and data acquisition processes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After this cours, the students should be able to describe main sensing principles for time, distance, angle, position, attitude, motion, temperature, optical imaging and spectrum; describe main performance criteria of sensors and sensor systems for static and dynamic geospatial applications; control s ensors for geospatial data acquisition using a computer and self-written programs; predict the performance of sensors and sensor systems based on information from data sheets and documentation of sensor system architecture; assess the performance of sensors and sensor systems experimentally. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course requires familiarity with linear algebra and analysis at the level of a BSc program in engineering or natural sciences. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0251-00L | Computational Methods for Geospatial Analysis | O | 4 credits | 4G | K. Schindler, J. A. Butt, B. Soja, Y. Xin | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to mathematical and statistical tools for geospatial data analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal is to familiarise students with the principles and tools of geospatial data analysis, and to enable them to apply those tools to practical tasks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course introduces basic methods of geostatistics and geospatial data analysis. Topics include spatial correlation, auto-correlation and the variogram; surface interpolation (kernel-based, kriging, parametric surface models); spatially adaptive filtering (bilinear, guided filter); spatial stochastic processes and random fields; time series models and spatio-temporal analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Bachelor level mathematics: analysis, linear algebra, statistics and probability theory, parameter estimation. Basic knowledge of multivariate statistics and machine learning is recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0417-00L | Transport Planning Methods | W | 6 credits | 4G | K. W. Axhausen | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course provides the necessary knowledge to develop models supporting and also evaluating the solution of given planning problems. The course is composed of a lecture part, providing the theoretical knowledge, and an applied part in which students develop their own models in order to evaluate a transport project/ policy by means of cost-benefit analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | - Knowledge and understanding of statistical methods and algorithms commonly used in transport planning - Comprehend the reasoning and capabilities of transport models - Ability to independently develop a transport model able to solve / answer planning problem - Getting familiar with cost-benefit analysis as a decision-making supporting tool | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course provides the necessary knowledge to develop models supporting the solution of given planning problems and also introduces cost-benefit analysis as a decision-making tool. Examples of such planning problems are the estimation of traffic volumes, prediction of estimated utilization of new public transport lines, and evaluation of effects (e.g. change in emissions of a city) triggered by building new infrastructure and changes to operational regulations. To cope with that, the problem is divided into sub-problems, which are solved using various statistical models (e.g. regression, discrete choice analysis) and algorithms (e.g. iterative proportional fitting, shortest path algorithms, method of successive averages). The course is composed of a lecture part, providing the theoretical knowledge, and an applied part in which students develop their own models in order to evaluate a transport project/ policy by means of cost-benefit analysis. Interim lab session take place regularly to guide and support students with the applied part of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Moodle platform (enrollment needed) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Willumsen, P. and J. de D. Ortuzar (2003) Modelling Transport, Wiley, Chichester. Cascetta, E. (2001) Transportation Systems Engineering: Theory and Methods, Kluwer Academic Publishers, Dordrecht. Sheffi, Y. (1985) Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods, Prentice Hall, Englewood Cliffs. Schnabel, W. and D. Lohse (1997) Verkehrsplanung, 2. edn., vol. 2 of Grundlagen der Strassenverkehrstechnik und der Verkehrsplanung, Verlag für Bauwesen, Berlin. McCarthy, P.S. (2001) Transportation Economics: A case study approach, Blackwell, Oxford. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0427-01L | Public Transport Design and Operations | W | 6 credits | 4G | F. Corman, T.‑H. Yan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course aims at analyzing, designing, improving public transport systems, as part of the overall transport system. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Public transport is a key driver for making our cities more livable, clean and accessible, providing safe, and sustainable travel options for millions of people around the globe. Proper planning of public transport system also ensures that the system is competitive in terms of speed and cost. Public transport is a crucial asset, whose social, economic and environmental benefits extend beyond those who use it regularly; it reduces the amount of cars and road infrastructure in cities; reduces injuries and fatalities associated to car accidents, and gives transport accessibility to very large demographic groups. Goal of the class is to understand the main characteristics and differences of public transport networks. Their various performance criteria based on various perspective and stakeholders. The most relevant decision making problems in a planning tactical and operational point of view At the end of this course, students can critically analyze existing networks of public transport, their design and use; consider and substantiate possible improvements to existing networks of public transport and the management of those networks; optimize the use of resources in public transport. General structure: general introduction of transport, modes, technologies, system design and line planning for different situations, mathematical models for design and line planning timetabling and tactical planning, and related mathematical approaches operations, and quantitative support to operational problems, evaluation of public transport systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Basics for line transport systems and networks Passenger/Supply requirements for line operations Objectives of system and network planning, from different perspectives and users, design dilemmas Conceptual concepts for passenger transport: long-distance, urban transport, regional, local transport Planning process, from demand evaluation to line planning to timetables to operations Matching demand and modes Line planning techniques Timetabling principles Allocation of resources Management of operations Measures of realized operations Improvements of existing services | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture slides are provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Ceder, Avi: Public Transit Planning and Operation, CRC Press, 2015, ISBN 978-1466563919 (English) Holzapfel, Helmut: Urbanismus und Verkehr – Bausteine für Architekten, Stadt- und Verkehrsplaner, Vieweg+Teubner, Wiesbaden 2012, ISBN 978-3-8348-1950-5 (Deutsch) Hull, Angela: Transport Matters – Integrated approaches to planning city-regions, Routledge / Taylor & Francis Group, London / New York 2011, ISBN 978-0-415-48818-4 (English) Vuchic, Vukan R.: Urban Transit – Operations, Planning, and Economics, John Wiley & Sons, Hoboken / New Jersey 2005, ISBN 0-471-63265-1 (English) Walker, Jarrett: Human Transit – How clearer thinking about public transit can enrich our communities and our lives, ISLAND PRESS, Washington / Covelo / London 2012, ISBN 978-1-59726-971-1 (English) White, Peter: Public Transport - Its Planning, Management and Operation, 5th edition, Routledge, London / New York 2009, ISBN 978-0415445306 (English) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0227-00L | Application Development in Cartography | W | 6 credits | 4G | L. Hurni | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course introduces concepts and techniques in 3D cartography and web application development. Practical experience will be gained in a map project. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students acquire general knowledge about the foundations and best practices in 3D cartography and modern web application development. They learn to plan, design and implement an interactive and animated 3D web map. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | - 3D cartography - Web mapping - Data processing - Animations and interactions - Map and UI design - Web application development - Programming (JavaScript). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Handouts of the lectures and exercise documents are available on Moodle. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Cartography II or Introduction to Web Cartography Part 1+2 (MOOC) or similar knowledge in mapping with JavaScript. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0287-00L | Image-based Mapping | W | 6 credits | 2G | K. Schindler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Application of photogrammetry and remote sensing methods for mapping and Earth observation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Learn how to apply photogrammetry, image analysis and machine learning to mapping tasks; hands-on experience in implementing automatic image analysis methods, and in judging their results. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Preprocessing of satellite images, atmospheric correction; extraction of features (radiometric indices, texture descriptors, etc.) from raw image intensities; semantic image segmentation (e.g., cloud masking); physical parameter estimation (e.g., vegetation height); practical deployment of geometric and semantic computer vision and image analysis methods for mapping; assessment of prediction results | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | basic knowledge of photogrammetry, image processing and machine learning | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 102-0617-00L | Basics and Principles of Radar Remote Sensing for Environmental Applications | W | 3 credits | 2G | I. Hajnsek | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course will provide the basics and principles of Radar Remote Sensing (specifically Synthetic Aperture Radar (SAR)) and its imaging techniques for the use of environmental parameter estimation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The course should provide an understanding of SAR techniques and the use of the imaging tools for bio/geophysical parameter estimation. At the end of the course the student has the understanding of 1. SAR basics and principles, 2. SAR polarimetry, 3. SAR interferometry and 4. environmental parameter estimation from multi-parametric SAR data | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course is giving an introduction into SAR techniques, the interpretation of SAR imaging responses and the use of SAR for different environmental applications. The outline of the course is the following: 1. Introduction into SAR basics and principles 2. Introduction into electromagnetic wave theory 3. Introduction into scattering theory and decomposition techniques 4. Introduction into SAR interferometry 5. Introduction into polarimetric SAR interferometry 6. Introduction into bio/geophysical parameter estimation (classification/segmentation, soil moisture estimation, earth quake and volcano monitoring, forest height inversion, wood biomass estimation etc.) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Handouts for each topic will be provided | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | First readings for the course: Woodhouse, I. H., Introduction into Microwave Remote Sensing, CRC Press, Taylor & Francis Group, 2006. Lee, J.-S., Pottier, E., Polarimetric Radar Imaging: From Basics to Applications, CRC Press, Taylor & Francis Group, 2009. Complete literature listing will be provided during the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 102-0627-00L | Applied Radar Remote Sensing | W | 3 credits | 2G | O. Frey | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course provides an introduction to processing and interpreting radar and synthetic aperture radar (SAR) remote sensing data. The primary topics of the course are interferometric techniques and related applications such as topography mapping and mapping of surface displacements, with a strong emphasis on solving practical problems using MATLAB. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Understand the concepts and techniques required to process and to adequately interpret interferometric radar/SAR data for topographic mapping and surface displacement applications. At the end of the course the student is able to read, display, process, and interpret interferometric radar/SAR using MATLAB. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The rationale behind the structure of the course follows the idea that radar imaging and radar/SAR interferometry are closely related and that a basic understanding of the radar imaging concept is helpful to understand and interpret interferometric radar data for various applications. The course starts with the real-aperture radar case and a first introduction to the concept of radar interferometry with applications to topographic mapping and mapping of surface displacements. Based on that, the 2-D imaging concept used in synthetic aperture radar imaging is treated. Then, we expand further on radar and SAR interferometric (InSAR) concepts and processing steps for single interferograms and stacks of interferograms also using persistent scatterer interferometry (PSI) to measure deformation based on time series of interferometric SAR data. Finally, the 3-D radar imaging case (SAR tomography) is put into context with PSI/InSAR time series as an extension of the more classical interferometric approaches thereby closing the circle around the strongly related concepts of SAR imaging and interferometry. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes/handouts for each topic will be provided online. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Additional reading material: Hanssen, R. F., Radar interferometry: data interpretation and error analysis, Kluwer Academic Publishers, 2001. ISBN: 978-0-306-47633-4 https://doi.org/10.1007/0-306-47633-9 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | It is highly recommended that the student has previously taken the following courses: 102-0617-00L: Basics and Principles of Radar Remote Sensing and 102-0617-01L: Methodologies for Image Processing of Remote Sensing Data | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 103-0687-00L | Cadastral Systems | W | 2 credits | 2G | J. Lüthy | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Conception, structure and impact of cadastral systems such as property cadastre, PLR-cadastre and related spatial data infrastructures (SDI) as well as their importance for civil society. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will get an understanding of the conception, structure and impact of cadastral systems and related concepts such as land administration, land registry, PLR-cadastre, spatial data infrastructures and Digital Twins. The link between cadastral systems, gender equality, economic prosperity and the contribution of property cadastre to achieving the United Nation Sustainable Development Goals (UN SDG) is discussed. The Swiss cadastral system ("Amtliche Vermessung") as well as a number of international systems in developed as well as in developing countries are discussed. The importance of the data from the property cadastre for the National Spatial Data Infrastructure (NSDI) and digital transformation will be investigated using various examples. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Origin and purpose of cadastral systems Importance of documentation of property information as a basis for economic development Basic concepts of cadastral systems (legal basis, conceptual principles, types of property, real estate types) Importance of cadastral systems for societal prosperity due to the impact on the economy, society and the environment. Contribution of the cadastre to the achievement of the UN SDGs on gender equality, poverty and food security. Swiss cadastral system - legal basis - organisation - Technical implementation - Quality and integrity assurance - profession - Embedding cadastral data in the national spatial data infrastructure Contribution of cadastral systems to the Digital Transformation of the society. Benchmarking and evaluations International trends (like blockchain), developments and initiatives to strengthen property rights, 3D cadastral system (above and below ground) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Larsson, G. (1991). Land Registration and Cadastral Systems: Tools for Land Dale, P., & McLaughlin, J. (1999). Land administration. Oxford University Press Yomralioglu, T., & McLaughlin, J. (Eds.). (2017). Cadastre: geo-information innovations in land administration (Vol. 335). Cham, Switzerland: Springer. UN-GGIM (2020), Integrated Geospatial Information Framework, Link | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 851-0724-01L | Real Estate Property Law  Particularly suitable for students of D-ARCH, D-BAUG, D-USYS. | W | 3 credits | 3V | S. Stucki, R. Müller-Wyss | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Real estate property law (esp. content, acquisition, restrictions under private and public law, transmission and loss). Legal presentation: land register, surveying, cadastre. Basic questions of contract and tax law. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The legal principles of real estate property law can be correctly interpreted and applied in daily life. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Real estate property law (esp. content, acquisition, restrictions under private and public law, transmission and loss). Legal presentation: land register, surveying, cadastre. Basic questions of contract and tax law. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Abgegebene Unterlagen: Skript in digitaler Form | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | - Adrian Mühlematter / Stephan Stucki: Grundbuchrecht für die Praxis, Zürich 2016 - Wolfgang Ernst / Samuel Zogg: Sachenrecht in a nutshell, Zürich 2020 - Jörg Schmid / Bettina Hürlimann-Kaup: Sachenrecht, Zürich 2017 - Meinrad Huser, Schweizerisches Vermessungsrecht, unter besonderer Berücksichtigung des Geoinformationsrecht und des Grundbuchrechts, Zürich 2014 - Meinrad Huser, Geo-Informationsrecht, Rechtlicher Rahmen für Geographische Informationssyteme, Zürich 2005 - Meinrad Huser, Darstellung von Grenzen zur Sicherung dinglicher Rechte, in ZBGR 2013, 238 ff. - Meinrad Huser, Baubeschränkungen und Grundbuch, in BR/DC 4/2016, 197 ff. - Meinrad Huser, Publikation von Eigentumsbeschränkungen - neue Regeln, in Baurecht 4/2010, S. 169 - Meinrad Huser, Der Aufteilungsplan im Stockwerkeigentum: Neue Darstellung – grössere Rechtsverbindlichkeit, in ZBGR 2020, S. 203 ff. - Meinrad Huser, Datenschutz bei Geodaten, in: Passadelis/Rosenthal/Thür, Datenschutzrecht, Basel 2015, S. 513 ff. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0187-01L | Space Geodesy | W | 6 credits | 4G | B. Soja | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | GNSS, VLBI, SLR/LLR and satellite altimetry: Principles, instrumentation and observation equation. Modelling and estimation of station coordinates and station motion. Ionospheric and tropospheric refraction and estimation of atmospheric parameters. Equation of motion of the unperturbed and perturbed satellite orbit. Perturbation theory and orbit determination. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After this course, the students should be able to • Describe the major observation techniques in space geodesy • Describe the necessary modeling and analysis approaches to derive geodetic products of highest quality • Select the appropriate space geodetic data for scientific investigations • Analyze the space geodetic data for scientific purposes • Interpret the scientific results | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Overview of GNSS, Very Long Baseline Interferometry (VLBI), Satellite and Lunar Laser Ranging (SLR/LLR), Satellite Radar Altimetry with the basic principles, the instruments and observation equations. Modelling of the station motions and the estimation of station coordinates. Basics of wave propagation in the atmosphere. Signal propagation in the ionosphere and troposphere for the different observation techniques and the determination of atmospheric parameters. Equation of motion of the unperturbed and perturbed satellite orbit. Osculating and mean orbital elements. General and special perturbation theory and the determination of satellite orbits. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Script M. Rothacher "Space Geodesy" | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0258-00L | Interoperability of GIS | W | 3 credits | 2G | J. Schito | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course deepens the understanding of two main interoperability principles used in Geographic Information Science. Students will expand their knowledge of databases and the Swiss standard INTERLIS and will learn to use different tools and mechanisms to transform geodata between different systems: file-based, by web services, or using a model-based approach to define data meaning semantically. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | 1. Develop a comprehensive understanding of the key principles of integrability in Geographic Information Science and apply them to geospatial data. 2. Explore the principles of syntactic and semantic interoperability and apply them to geospatial data using a variety of tools. 3. Gain an in-depth understanding of geodatabases, UML, INTERLIS, and of the model-driven data transfer with restructuring and apply this knowledge to geodata. 4. Analyze the ontological spectrum of interoperability principles with varying levels of semantic expressiveness and different formalisms. 5. Examine the historical development of Geographic Information Systems interoperability, including the evolution of different approaches used across different countries. 6. Apprehend and foster research skills and improve competences in scientific writing and communication through completion of a voluntary project work. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The aim of this course is to provide students with a deep understanding of two key interoperability principles in Geographic Information Science. Throughout the course, students will be exposed to a range of tools and mechanisms used to transform geospatial content across different file structures and databases. In particular, we will focus on the Conceptual Schema Language INTERLIS, which is used in Swiss surveying, while developing students’ abilities of interpreting, defining, and working with such models, also by using free and open-source tools. Furthermore, we will explore the concept of integrability, which is fundamental to establishing higher levels of interoperability. We will examine how interoperability can span an ontological spectrum from OGC Web Services to semantic transformation, which may one day be understood by machines. By the end of this course, students will have gained a comprehensive understanding of the principles of interoperability and their applications in Geographic Information Science. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Completed Bachelor course in GIS II or Geoinformationstechnologien und -analysen (GTA) and familiarity of working with a GIS and with geodatabases. Since we will primarily be using QGIS and PostgreSQL (pgAdmin), it would be beneficial if you could bring your own device with both applications pre-installed. Although not compulsory, it may also be useful to have Python/Anaconda and certain geospatial processing libraries installed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0778-00L | GIS and Geoinformatics Lab | W | 4 credits | 4P | P. Kiefer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Independent study project with novel geoinformation technologies. Information on past projects: http://gis-lab.ethz.ch/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This lab focuses on presenting spatial, temporal, and open data in tangible ways. Students will learn how to work with novel geoinformation technologies such as virtual/mixed reality or mobile applications. They will engage in teamwork, application design, programming and presenting their results. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-5905-00L | Mixed Reality | W | 5 credits | 3G + 1A | C. Holz, M. Pollefeys | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The goal of this course is an introduction and hands-on experience on latest mixed reality technology at the cross-section of 3D computer graphics and vision, human machine interaction, as well as gaming technology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After attending this course, students will: 1. Understand the foundations of 3D graphics, Computer Vision, and Human-Machine Interaction 2. Have a clear understanding on how to build mixed reality apps 3. Have a good overview of state-of-the-art Mixed Reality 4. Be able to critically analyze and asses current research in this area. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The course introduces latest mixed reality technology and provides introductory elements for a number of related fields including: Introduction to Mixed Reality / Augmented Reality / Virtual Reality Introduction to 3D Computer Graphics, 3D Computer Vision. This will take place in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on mixed reality topics, where small groups of students will work on a particular project with the goal to design, develop and deploy a mixed reality application. The project topics are flexible and can reach from proof-of-concept vision/graphics/HMI research, to apps that support teaching with interactive augmented reality, or game development. The default platform will be Microsoft HoloLens in combination with C# and Unity3D - other platforms are also possible to use, such as tablets and phones. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites include: - Good programming skills (C# / C++ / Java etc.) - Computer graphics/vision experience: Students should have taken, at a minimum, Visual Computing. Higher level courses are recommended, such as Introduction to Computer Graphics, 3D Vision, Computer Vision. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 363-0790-00L | Technology Entrepreneurship | W | 2 credits | 2V | F. Hacklin | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Technology ventures are significantly changing the global economic picture. Technological skills increasingly need to be complemented by entrepreneurial understanding. This course offers the fundamentals in theory and practice of entrepreneurship in new technology ventures. Main topics covered are success factors in the creation of new firms, including founding, financing and growing a venture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This course provides theory-grounded knowledge and practice-driven skills for founding, financing, and growing new technology ventures. A critical understanding of dos and don'ts is provided through highlighting and discussing real life examples and cases. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Weekly sessions - recorded. 10+ sessions carried out by guest lecturers: experts in the broad field of technology entrepreneurship (e.g., serial entrepreneurs, venture capitalists, (E)MBA professors, company builders, patent experts, scale-up executives, …). Final session: multiple choice semester assignment (100% of grade). Typical lecture format (2h): 15': Introduction 60': Guest testimonial 15': Discussion related to topic (in groups) 10': Plenary discussion 20': Q&A with (guest) lecturer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture slides and case material | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0787-00L | Project Parameter Estimation | W | 3 credits | 2P | J. A. Butt, T. Medic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Solving engineering problems with modern methods of parameter estimation for network adjustment in a real-world scenario; choosing adequate mathematical models, implementation and assessment of the solutions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Learn to solve engineering problems with modern methods of parameter estimation in a real-world scenario. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Analysis of given problems, selection of appropriate mathematical modells, implementation and testing using Matlab: Kriging; system calibration of a terrestrial laser scanner. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The task assignments and selected documentation will be provided as PDF. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisite: Statistics and Probability Theory, Geoprocessing and Parameterestimation, Geodetic Reference Systems and Networks | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 103-0747-00L | Cartography Lab | W | 6 credits | 13A | L. Hurni | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Independent semester work in cartography | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Independent semester work in cartography | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Choice of theme upon individual agreement | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Cartography III Multimedia Cartography Further information at http://www.karto.ethz.ch/studium/lehrangebot.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0820-00L | Introduction to Scientific Computation | W | 3 credits | 2G | M. Usvyatsov | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to tools, techniques, and methods for data processing and analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Get ready to work with data of different origin. Learn Python and tools to the level which allows attacking data related problems. Basic introduction to numerical algorithms for efficient problem solving | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Python for scientific programming, fast numerical computations and data visualisation. Libraries for data processing. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Basic probability theory and statistics, linear algebra, basic programming skills | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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