Konrad Schindler: Catalogue data in Autumn Semester 2021

Name Prof. Dr. Konrad Schindler
FieldPhotogrammetry
Address
I. f. Geodäsie u. Photogrammetrie
ETH Zürich, HIL D 42.3
Stefano-Franscini-Platz 5
8093 Zürich
SWITZERLAND
Telephone+41 44 633 30 04
E-mailschindler@ethz.ch
URLhttps://igp.ethz.ch/personen/person-detail.html?persid=143986
DepartmentCivil, Environmental and Geomatic Engineering
RelationshipFull Professor

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.
103-0254-AALPhotogrammetry
Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement.

Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit.
6 credits13RK. Schindler
AbstractThe class conveys the basics of photogrammetry. Its aim is to equip students with an understanding of the principles, methods and applications of image-based 3D measurement.
ObjectiveThe aim is an understanding of the principles, methods and possible applications of photogrammetry. The course also forms the basis for more in-depth studies and self-reliant photogrammetric project work in further photogrammetry-related courses.
ContentThe basics of photogrammetry, its products and applications: the principle of image-based triangulation; digital aerial cameras and related sensors; projective geometry; mathematical modelling, calibration and orientation of cameras; photogrammetric reconstruction of points and lines, and stereoscopy; orthophoto generation; digital photogrammetric workstations; recording geometry and flight planning
Literature- Luhmann , Robson, Kyle, Boehm: Close-Range Photogrammetry and 3D Imaging, deGruyter, 2020
- Foerstner, Wrobel: Photogrammetric Computer Vision, Springer, 2016
Prerequisites / NoticeRequirements: basic knowledge of physics, linear algebra and analytical geometry, calculus, least-squares adjustment and statistics
103-0287-00LImage Interpretation4 credits3GK. Schindler
AbstractApplication 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
ObjectiveLearn 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.
ContentPreprocessing 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 / Noticebasic knowledge of machine learning; basic knowledge of image processing
103-0817-00LGeomatics Seminar Restricted registration - show details 4 credits2SK. Schindler, K. W. Axhausen, A. Grêt-Regamey, L. Hurni, W. Kuhn, M. Rothacher, A. Wieser
AbstractIntroduction to general scientific working methods and skills in the core fields of geomatics. It includes a literature study, a review of one of the articles, a presentation and a report about the literature study.
ObjectiveLearn how to search for literature, how to write a scientific report, how to present scientific results, and how to critically read and review a scientific article.
ContentA list of topics for the literature study are made available at the beginning of the semester. A topic can be selected based on a moodle.
Prerequisites / NoticeAgreement with one of the responsible Professors is necessary.
103-0849-AALMultivariate Statistics and Machine Learning
Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement.

Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit.
4 credits9RK. Schindler
AbstractIntroduction to statistical modelling and machine learning.
ObjectiveThe goal is to familiarise students with the principles and tools of machine learning, and to enable them to apply them for practical data analysis.
Contentmultivariate probability distributions; comparison of distributions; regression; classification; model selection and cross-validation; clustering and density estimation; mixture models; neural networks
Literature- Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer 2009
- Bishop: Pattern Recognition and Machine Learning, Springer 2006
- Duda, Hart, Stork: Pattern CLassification, Wiley 2012