Konrad Schindler: Catalogue data in Spring Semester 2025 |
| Name | Prof. Dr. Konrad Schindler |
| Field | Photogrammetry |
| 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 |
| schindler@ethz.ch | |
| URL | https://igp.ethz.ch/personen/person-detail.html?persid=143986 |
| Department | Civil, Environmental and Geomatic Engineering |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 101-0510-10L | Project Work Geospatial Engineering | 4 credits | 8A | K. Schindler, J. Lordieck | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This project-based course allows the students to get insights into a selected real-world challenge in Geospatial Engineering. Besides the methodical and subject related competences which the students acquire, the course aims particularly at enhancing a variety of transferable skills, above all teamwork, critical thinking and communication. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | -The students know the basic rules of scientific work and integrity, and apply them to their work. -The students know and apply success factors for teamwork including team roles, team phases and reflection. -The students know and apply the elements of critical thinking, they identify and reflect their own position within discussions. -The students have obtained insight into a selected challenge within Geospatial Engineering and are able to share this insight with their fellow students. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The students chose one of the offered topics. They work on the topic in groups of about 4 students. The team members discuss and plan their roles, they take initiative and responsibility for the team result such that the project goals can be achieved. At the beginning of the semester there is an introduction to scientific working for all students. The groups carry out the project at least partially during times individually agreed upon by the group members. At pre-arranged times there is an exchange with the supervisors and with the other groups. The results are documented in a report and on a poster, and are presented tot he other students. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | There is no script. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Documents and literature recommendations are handed out during the semester by the supervisors, according to the chosen topic. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | There are no special conditions or prerequisites. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering  | 1 credit | 1S | M. Lukovic, E. Chatzi, F. Corman, I. Hajnsek, V. Ntertimanis, 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0523-15L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering | 1 credit | 1G | M. Lukovic, E. Chatzi, F. Corman, I. Hajnsek, V. Ntertimanis, 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 103-0274-01L | Image Processing | 3 credits | 2G | K. Schindler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to basic concepts and methods of digital image processing. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students - can understand and explain the mathematical and statistical representation of images - understand and recognise digital image processing as a basis for remote sensing, photogrammetry and computer vision - know and understand basic operations for digital image and signal processing - are able to select and apply suitable computational methods for basic image processing tasks - are able to solve image processing tasks with the presented tools | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | • digital images, signal processing, sampling • geometric transformations • colour and contrast • image filtering, image restoration and enhancement • point- and line detection • similarity measures and image matching | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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| 103-0798-00L | Geodetic Project Course Crediting in the 2013 curriculum: Elective Courses Crediting in the 2022 curriculum: Core Electives | 6 credits | 9P | B. Soja, K. Schindler, A. Wieser | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Field course with practical geodetic projects (3 weeks) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Planning, implementation and presentation of a geodetic project, including field work | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Single-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 / Notice | The course will take place again in FS 2025. Within the block course of 3 weeks, approximately 2 weeks will be dedicated to fieldwork and one week to preparatory work and post-processing. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0849-AAL | Multivariate 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 credits | 9R | K. Schindler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to statistical modelling and machine learning. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal is to familiarise students with the principles and tools of machine learning, and to enable them to apply them for practical data analysis. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | multivariate 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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0849-00L | Multivariate Statistics and Machine Learning | 4 credits | 3G | K. Schindler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to statistical modelling and machine learning. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal is to familiarise students with the principles and tools of machine learning, and to enable them to apply them for practical data analysis. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | multivariate probability distributions; comparison of distributions; regression; classification; model selection and cross-validation; clustering and density estimation; mixture models; neural networks | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | C. 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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0851-00L | Photogrammetry | 6 credits | 5G | K. Schindler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The class conveys the basics of photogrammetry. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The aim is to equip students with an in-depth understanding of the principles, methods and applications of image-based 3D measurement and mapping. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Basics 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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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