Benedikt Soja: Catalogue data in Autumn Semester 2023 |
| Name | Prof. Dr. Benedikt Soja |
| Field | Space Geodesy |
| benedikt.soja@geod.baug.ethz.ch | |
| URL | http://twitter.com/b_soja |
| Department | Civil, Environmental and Geomatic Engineering |
| Relationship | Assistant Professor (Tenure Track) |
| Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||
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| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering  | 1 credit | 1S | V. Ntertimanis, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, B. Soja, M. J. Van Strien | |||||||||||||||||||||||||||||||||||||||||
| Abstract | Current research in machine learning and data science within the research fields of the department. The goal is to learn about current research projects at our department, to strengthen our expertise and collaboration with respect to data-driven models and methods, to provide a platform where research challenges can be discussed, and also to practice scientific presentations. | ||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | - learn about discipline-specific methods and applications of data science in neighbouring fields - network people and methodological expertise across disciplines - establish links and discuss connections, common challenges and disciplinespecific differences - practice presentation and discussion of technical content to a broader, less specialised scientific audience | ||||||||||||||||||||||||||||||||||||||||||||
| Content | Current research at D-BAUG will be presented and discussed. | ||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This doctoral seminar is intended for doctoral students affiliated with the Department of Civil, Environmental and Geomatic Engineering. Other students who work on related topics need approval by at least one of the organisers to register for the seminar. Participants are expected to possess elementary skills in statistics, data science and machine learning, including both theory and practical modelling and implementation. The seminar targets students who are actively working on related research projects. | ||||||||||||||||||||||||||||||||||||||||||||
| 101-0523-14L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering | 1 credit | 1G | V. Ntertimanis, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, B. Soja, M. J. Van Strien | |||||||||||||||||||||||||||||||||||||||||
| Abstract | This doctoral seminar organised by the D-BAUG platform on data science and machine learning aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms). | ||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students will • Critically read scientific papers on the recent developments in machine learning • Put the research in context • Present the contributions • Discuss the validity of the scientific approach • Evaluate the underlying assumptions • Evaluate the transferability/adpatability of the proposed approaches to own research • (Optionally) implement the proposed approaches. | ||||||||||||||||||||||||||||||||||||||||||||
| Content | With the increasing amount of data collected in various domains, the importance of data science in many disciplines, such as infrastructure monitoring and management, transportation, spatial planning, structural and environmental engineering, has been increasing. The field is constantly developing further with numerous advances, extensions and modifications. The course aims at discussing recent research papers in the field of machine learning and analyzing the transferability/adaptability of the proposed approaches to applications in the field of civil and environmental engineering (if possible and applicable, also implementing the adapted algorithms). Each student will select a paper that is relevant for his/her research and present its content in the seminar, putting it into context, analyzing the assumptions, the transferability and generalizability of the proposed approaches. The students will also link the research content of the selected paper to the own research, evaluating the potential of transferring or adapting it. If possible and applicable, the students will also implement the adapted algorithms The students will work in groups of three students, where each of the three students will be reading each other’s selected papers and providing feedback to each other. | ||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This doctoral seminar is intended for doctoral students affiliated with the Department of Civil, Environmental and Geomatic Engineering. Other students who work on related topics need approval by at least one of the organisers to register for the seminar. Participants are expected to possess elementary skills in statistics, data science and machine learning, including both theory and practical modelling and implementation. The seminar targets students who are actively working on related research projects. | ||||||||||||||||||||||||||||||||||||||||||||
| 103-0187-01L | Space Geodesy | 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" | ||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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| 103-0251-00L | Computational Methods for Geospatial Analysis | 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. | ||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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