Benedikt Soja: Catalogue data in Autumn Semester 2021

Name Prof. Dr. Benedikt Soja
FieldSpace Geodesy
E-mailbenedikt.soja@geod.baug.ethz.ch
URLhttp://twitter.com/b_soja
DepartmentCivil, Environmental and Geomatic Engineering
RelationshipAssistant Professor (Tenure Track)

NumberTitleECTSHoursLecturers
101-0522-10LDoctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering Restricted registration - show details
Does not take place this semester.
Number of participants limited to 21.
1 credit2SB. Soja, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, K. Schindler
AbstractCurrent 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.
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
ContentCurrent research at D-BAUG will be presented and discussed.
Prerequisites / NoticeThis 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.
101-0523-12LFrontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (HS21) Restricted registration - show details
Number of participants limited to 21.
1 credit2SM. A. Kraus, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. Lukovic, K. Schindler, B. Soja, B. Sudret, M. J. Van Strien
AbstractThis 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).
ObjectiveStudents 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.
ContentWith 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 / NoticeThis 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.
103-0627-00LSpace Geodesy Lab5 credits3PG. Möller, R. Hohensinn, M. Rothacher, B. Soja
AbstractSpace Geodesy Lab allows you to deepen your knowledge about space-geodetic techniques, in particular of GNSS, VLBI, SLR, satellite altimetry and gravity missions for monitoring the environment and changes within the Earth system.
ObjectiveStudents enrolled in this course will be given the possibility to learn about space-geodetic methods to solve a specific research problem. As a result, you will become familiar with the entire processing chain from gathering of raw measurements to geodetic products like reference frames, station motions, Earth orientation parameters, atmospheric and climate variables, or the Earth gravity field and its variations.
ContentFor a small project based on space geodetic measurements and methods (or a related project of your choice), you or a group of 2-3 students will be provided with the necessary equipment, access to data and analysis tools for solving a research question. Therefore, we expect autonomous development, planning, data analysis and interpretation of the results. At the end of the semester you will be ask to present your findings and to submit a report summarizing your semester activities. As needed, further background will be given during the semester.
Lecture notesdiv. sources
LiteratureM. Rothacher – Space Geodesy lecture notes
Additional literature will be distributed during lectures
Prerequisites / NoticeBasic knowledge about satellite geodesy, reference frames and the Earth gravity field. Programming skills in Matlab, Python or similar.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingassessed
Media and Digital Technologiesfostered
Problem-solvingassessed
Project Managementassessed
Social CompetenciesCommunicationassessed
Cooperation and Teamworkassessed
Customer Orientationfostered
Leadership and Responsibilityfostered
Self-presentation and Social Influence assessed
Sensitivity to Diversityfostered
Negotiationfostered
Personal CompetenciesAdaptability and Flexibilityassessed
Creative Thinkingassessed
Critical Thinkingassessed
Integrity and Work Ethicsfostered
Self-awareness and Self-reflection fostered
Self-direction and Self-management fostered
103-0657-01LSignal Processing, Modeling, Inversion3 credits2GB. Soja
AbstractTopics related to time series analysis, modeling, parameter estimation, prediction, and interpretation. Theoretical concepts will be applied to geodetic problems.
ObjectiveStudents have various methods at hand to mathematically formulate specific scientific problems. They are able to analyse observational data, estimate numerical and analytical models, and predict parameters into the future. The students can evaluate and interpret measurements and models derived from them. They know the necessary terminology in order to study expert literature.
ContentTopics covered in this lecture include: time series analysis, Fourier transformation, stochastic processes, ARMA, analytical and numerical modeling, model selection, linear and non-linear parameter estimation, sequential parameter estimation and filtering, machine learning for time series analysis and prediction, interpretation of measurements and derived results. The theoretical concepts will be illustrated by concrete examples commonly found in geodetic applications.
Lecture notesLecture slides and notes
LiteratureScript Alain Geiger: Geoprocessing
Additional literature will be referred to in class
Prerequisites / NoticeCourses corresponding to: Analysis I+II, Linear Algebra I, Parameter Estimation