Konrad Schindler: Catalogue data in Spring 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
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
Number of participants limited to 21.
1 credit2SB. Soja, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. A. Kraus, M. Lukovic, K. Schindler, M. J. Van Strien
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.
101-0523-11LFrontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (FS21) Restricted registration - show details
Number of participants limited to 21.
1 credit2SM. Lukovic, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. A. Kraus, K. Schindler, B. Soja, 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. It shall equip students with basic knowledge of the principles, methods and applications of image-based measurement.
ObjectiveUnderstanding 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 courses.
ContentFundamental concepts of photogrammetry, its products and applications: the principle of image-based measurement; digital aerial cameras and related sensors; projective geometry; mathematical modeling, calibration and orientation of cameras; photogrammetric 3D reconstruction and stereoscopy; digital photogrammetric workstations; recording geometry and flight planning
Lecture notesPhotogrammetry - Basics (slides on the web)
Exercise material (on the web)
Literature- Kraus, K.: Photogrammetrie, Band 1: Geometrische Informationen aus Photographien und Laserscanneraufnahmen, mit Beiträgen von Peter Waldhäusl, Walter de Gruyter Verlag, Berlin, 7th edition
- Kraus, K.: Photogrammetrie, Band 2: Verfeinerte Methoden und Anwendungen, mit Beiträgen von J. Jansa und H. Kager, Walter de Gruyter Verlag, Berlin, 3rd edition
- Thomas Luhmann: Nahbereichsphotogrammetrie. Grundlagen, Methoden und Anwendungen, H. Wichmann Verlag, Karlsruhe, 2nd edition 2003
- Richard Hartley and Andrew Zisserman: Multiple View Geometry, Cambridge University Press; 2nd edition 2004
Prerequisites / NoticeRequirements: knowledge of physics, linear algebra and analytical geometry, calculus, least-squares adjustment and statistics, basic programming skills.
103-0274-AALImage Processing
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.
3 credits6RK. Schindler, J. D. Wegner
AbstractThe objective of this lecture is to introduce the basic concepts of image formation and explain the basic methods of signal and image processing.
ObjectiveUnderstanding core methods and algorithms in image processing and computer vision and the underlying signal processing foundations.
Applying image processing algorithms to relevant problems in photogrammetry and remote sensing.
ContentThe following topics will be covered in the course:
- Properties of digital images
- Signal processing/Sampling
- Image enhancement
- Image restoration: Spatial domain
- Image restoration: Fourier domain
- Color/Demosaicing
- Image compression
- Feature extraction
- Texture analysis
- Image segmentation
Lecture notesA script will be provided as PDF files on the lecture website.
LiteratureWe suggest the following textbooks for further reading:

Rafael C. Gonzalez, Richard E. Woods
Digital Image Processing
Prentice Hall International, 2008
ISBN: 013168728X

Rafael C. Gonzalez, Steven L. Eddins, Richard E. Woods:
Digital Image Processing Using MATLAB
Prentice Hall, 2003
ISBN: 0130085197
Prerequisites / NoticeThe lecture is accompanied by programming assignments, that need to be completed in order to pass the course.
103-0274-01LImage Processing3 credits2GK. Schindler, J. D. Wegner
AbstractIntroduction to basic concepts and methods of digital image processing.
Objective- Mathematical and statistical description of images
- knowledge of basic algorithms for digital image and signal processing
- familiarity with fundamental image processing operations
- selection and application of suitable computational methods for basic image processing tasks
- understanding of digital image processing as a basis for remote sensing, photogrammetry and computer vision
Content• Digitale Bilder, Signalprozessierung, Abtastung
• Geometrische Transformationen
• Farbräume
• Filterung von Bildern, Bildrestauration und -verbesserung
• Morphologische Operationen
• Punkt- und Liniendetektion
• Ähnlichkeitsmasse und Matching von Bildern
• Bildsegmentierung
• Radiometrie von Satellitenbildern
103-0798-00LGeodetic Project Course Restricted registration - show details
Number of participants limited to 24.
5 credits9PM. Rothacher, K. Schindler, A. Wieser
AbstractField course with practical geodetic projects (3 weeks)
ObjectiveField course with practical geodetic projects (3 weeks)
ContentSingle-handed treatment of current geodetic projects in small teams. Writing of a technical report with description of the project, calculations, results and interpretations. Possibility to continue the work in a master's thesis or project.
Prerequisites / NoticeThe course takes place bezween June 14 and July 9, 2021. Within this period, 2 weeks of fieldwork in Graubünden are planned. Additionally there will be preparatory work and post-processing carried out in Zurich.
103-0848-00LIndustrial Metrology and Machine Vision Restricted registration - show details
Number of participants limited to 30.
4 credits3GK. Schindler, D. Salido Monzú
AbstractThis course introduces contact and non-contact techniques for 3D coordinate, shape and motion determination as used for 3D inspection, dimensional control, reverse engineering, motion capture and similar industrial applications.
ObjectiveUnderstanding the physical basis of photographic sensors and imaging; familiarization with a broader view of image-based 3D geometry estimation beyond the classical photogrammetric approach; understanding the concepts of measurement traceability and uncertainty; acquiring an overview of general 3D image metrology including contact and non-contact techniques (coordinate measurement machines; optical tooling; laser-based high-precision instruments).
ContentCCD and CMOS technology; structured light and active stereo; shading models, shape from shading and photometric stereo; shape from focus; laser interferometry, laser tracker, laser radar; contact and non-contact coordinate measurement machines; optical tooling; measurement traceability, measurement uncertainty, calibration of measurement systems; 3d surface representations; case studies.
Lecture notesLecture slides and further literature will be made available on the course webpage.
103-0849-00LMultivariate Statistics and Machine Learning Restricted registration - show details
Number of participants limited to 40.
4 credits4GK. 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
LiteratureC. Bishop: Pattern Recognition and Machine Learning, Springer 2006
T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer 2017
R. Duda, P. Hart, D. Stork: Pattern Classification, Wiley 2000
103-0851-00LPhotogrammetry6 credits4GK. Schindler
AbstractThe class conveys the basics of photogrammetry.
ObjectiveThe aim is to equip students with an in-depth understanding of the principles, methods and applications of image-based 3D measurement.
ContentBasics of photogrammetry, its products and applications: the principle of image-based measurement; digital aerial cameras and related sensors; projective geometry; mathematical modeling, calibration and orientation of cameras; photogrammetric Triangulation and surface reconstruction; bundle adjustment; recording geometry and flight planning; airborne laser-scanning
Literature- Wolfgang Foerstner and Bernahrd Wrobel: Photogrammetric Computer Vision, Springer, 2016
- Thomas Luhmann, Stuart Robson, Stephen Kyle, Jan Boehm: Close-Range Photogrammetry and 3D Imaging, De Gruyter, 3rd edition 2019
- Richard Hartley and Andrew Zisserman: Multiple View Geometry, Cambridge University Press; 2nd edition 2004