Search result: Catalogue data in Autumn Semester 2021
Geomatics Master | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Major Courses | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Major in Engineering Geodesy and Photogrammetry | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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103-0287-00L | Image Interpretation | O | 4 credits | 3G | K. Schindler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Application of machine learning in satellite-based Earth observation; methodological and practical aspects of remote sensing data analysis, including atmospheric correction, image feature extraction, image classification and segmentation, regression of physical parameters | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | Learn how to apply image analysis and machine learning to image interpretation tasks in remote sensing; 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 classical machine learning algorithms as well as deep neural networks for remote sensing data analysis; assessment of prediction results | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | basic knowledge of machine learning; basic knowledge of image processing | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
103-0137-00L | Engineering Geodesy | O | 4 credits | 3G | A. Wieser, J. Qiao | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Introduction to Engineering Geodesy: methods, instruments, and applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | The students will be introduced to the methods, instruments and applications in Engineering Geodesy with a focus on end-to-end quality assessment, sensor and multi-sensor-systems, setting out, and monitoring of engineering objects. They will be able to acquire enhanced knowledge and fundamental competences in high-precision angle, distance and height measurements. They will be introduced to aspects of interdisciplinary work in particular related to construction processes and civil engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | - Introduction: Definition, methods, and tasks - Planning and realizing geodetic networks - High precision distance, angle and height measurements - Sensors and multi-sensor-systems - Calibration and testing - Engineering Geodesy in construction above and below ground - Tunnel surveying - Building Information Modeling (BIM) - Deformation monitoring: Models, methods, and applications | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | The slides and additional documents will be provided in electronic form. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Kavanagh B.F. (2010) Surveying with Construction Applications. Prentice Hall. Schofield W., Breach M. (2007) Engineering Surveying. Elsevier Ltd. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Fundamental knowledge in geodetic metrology (applied geodesy), physical geodesy, reference systems, GNSS and parameter estimation is required for this course. This knowledge can for instance been acquired within the appropriate courses of the bachelor studies in Geomatics and Planning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
103-0267-01L | Photogrammetry and 3D Vision Lab Prerequisites: It is suggested that students take the course "Photogrammetrie" at bachelor level before this one. | W | 3 credits | 2G | C. Albl | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The aim of the course is to provide a hands-on experience with close-range photogrammetry. The students will go through all aspects of 3D reconstruction starting with the image acquisition, camera calibration, automatic sparse geometry reconstruction, and eventually produce a final textured 3D model. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | The aim of the course is to familiarize the students with both the practical aspects of close-range photogrammetric reconstruction and the theoretical foundations behind them. After passing the course, the students should be able to plan the image acquisition, perform the camera calibration, build a structure-from-motion pipeline using modern open-source libraries, produce a 3D model, and improve its quality. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | This course builds in part on the courses "Photogrammetrie" and "Bildverarbeitung" from the Bachelor program. It focuses on the particular challenges of automated close-range photogrammetry. The students will obtain their own images using their own cameras/smartphones, learn how to perform the camera calibration, implement some key and interesting parts of the automatic reconstruction pipeline and learn how to avoid and address common issues in 3D reconstruction. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Presentation slides, necessary publications and complementary learning materials will be provided through a dedicated course web-site. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Recommended textbooks: - T. Luhmann. Nahbereichsphotogrammetrie (also available in English ) - R. Hartley and A. Zisserman. Multi-view geometry in computer vision - R. Szeliski. Computer Vision | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | A recommended prerequisite for taking this course are the Bachelor courses "Photogrammetrie" and "Bildverarbeitung". If you have not passed them, please contact the main lecturer of the course before enrolling. The course will include both practical work with commercial software, and programming in Python. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
103-0787-00L | Project Parameter Estimation | W | 3 credits | 3P | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
103-0687-00L | Cadastral Systems | W | 2 credits | 2G | D. M. Steudler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Nature, role and importance of cadastral systems and related concepts such as land administration, land registration and spatial data infrastructures (SDIs). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | The students will get an understanding of the nature, role and importance of cadastral systems and related concepts such as land administration, land registration and spatial data infrastructures (SDIs). The Swiss cadastral system as well as a range of international approaches both in developed and developing countries will be reviewed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Origins and purposes of cadastral systems Importance of documentation Basic concepts of cadastral systems (real estate, legal basis, conceptual principles, property-ownership, property types) Swiss cadastral system: - legal basis - organization - technical elements - methods of data acquisition and maintenance - profession - quality assurance Digital revolution, access to data Benchmarking and evaluation of cadastral systems International trends, developments and initiatives | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | see: Link | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Larsson, G. (1991). Land Registration and Cadastral Systems: Tools for Land Information and Management. Harlow, Essex, England: Longman Scientific and Technical, New York: Wiley, ISBN 0-582-08952-2, 175 p. see also: Link | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
263-5902-00L | Computer Vision | W | 8 credits | 3V + 1U + 3A | M. Pollefeys, S. Tang, F. Yu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The goal of this course is to provide students with a good understanding of computer vision and image analysis techniques. The main concepts and techniques will be studied in depth and practical algorithms and approaches will be discussed and explored through the exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | The objectives of this course are: 1. To introduce the fundamental problems of computer vision. 2. To introduce the main concepts and techniques used to solve those. 3. To enable participants to implement solutions for reasonably complex problems. 4. To enable participants to make sense of the computer vision literature. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Camera models and calibration, invariant features, Multiple-view geometry, Model fitting, Stereo Matching, Segmentation, 2D Shape matching, Shape from Silhouettes, Optical flow, Structure from motion, Tracking, Object recognition, Object category recognition | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | It is recommended that students have taken the Visual Computing lecture or a similar course introducing basic image processing concepts before taking this course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
851-0724-01L | Real Estate Property Law Particularly suitable for students of D-ARCH, D-BAUG, D-USYS | W | 3 credits | 3V | M. Huser, R. Müller-Wyss, S. Stucki | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Fundamental concepts of Land Register Law and Land Surveying Law (substantive and procedural rules of Land Register Law, the parts and the relevance of the Land Register, process of registration with the Land Register, legal problems of land surveying, reform of the official land surveying). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Objective | Overview of the legal norms of land registry and surveying law. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Basic principles of material and formal land registry law, components of the land register, consequences of the land register, the registration process, legal problems of surveying, the reform of official surveying, liability of the geom-eter. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Abgegebene Unterlagen: Skript in digitaler Form Pflichtlektüre: Meinrad Huser, Schweizerisches Vermessungsrecht, unter besonderer Berücksichtigung des Geoinformationsrechts und des Grundbuchrechts, Beiträge aus dem Institut für schweizerisches und internationales Baurecht der Universität Freiburg/Schweiz, Zürich 2014 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | - 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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