Konrad Schindler: Catalogue data in Autumn Semester 2020 |
| 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 | |
|---|---|---|---|---|---|
| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering  Number of participants limited to 21. | 1 credit | 2S | K. Schindler, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. A. Kraus, M. Lukovic, M. Raubal, B. Soja, B. Sudret | |
| 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. | ||||
| 101-0523-10L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering Number of participants limited to 21. | 1 credit | 2S | O. Fink, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, M. Raubal, K. Schindler, B. Soja, B. Sudret | |
| 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. | ||||
| 103-0254-AAL | Photogrammetry 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 credits | 13R | K. Schindler | |
| Abstract | The class conveys the basics of photogrammetry. Its aim is to equip students with an understanding of the principles, methods and applications of image-based measurement. | ||||
| Learning objective | The 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 courses. | ||||
| Content | The 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 reconstruction of points and lines, and stereoscopy; orthophoto generation; digital photogrammetric workstations; recording geometry and flight planning | ||||
| Lecture notes | Photogrammetry (slides 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 / Notice | Requirements: knowledge of physics, linear algebra and analytical geometry, calculus, least-squares adjustment and statistics, basic programming skills. | ||||
| 103-0287-00L | Image Interpretation | 4 credits | 3G | K. Schindler | |
| Abstract | Introduction to interactive, semi-automatic and automatic methods for image interpretation and data analysis; methodological aspects of computer-assisted remote sensing, including semantic image classification and segmentation; detection and extraction of individual objects; estimation of physical parameters. | ||||
| Learning objective | Understanding the tasks, problems, and applications of image interpretation; basic introduction of computational methods for image-based classification and parameter estimation (clustering, classification, regression), with focus on remote sensing. | ||||
| Content | Image (and point-cloud) interpretation tasks: semantic classification (e.g. land-cover mapping), physical parameter estimation (e.g. forest biomass); Image coding and features; probabilistic inference, generative and discriminative models; clustering and segmentation; continuous parameter estimation, regression; classification and labeling; deep learning; atmospheric influences in satellite remote sensing; | ||||
| Literature | J. A. Richards: Remote Sensing Digital Image Analysis - An Introduction C. Bishop: Pattern Recognition and Machine Learning | ||||
| Prerequisites / Notice | basics of probability theory and statistics; basics of image processing; elementary programming skills (Matlab); | ||||
| 103-0817-00L | Geomatics Seminar | 4 credits | 2S | M. Raubal, A. Grêt-Regamey, L. Hurni, M. Rothacher, K. Schindler, A. Wieser | |
| Abstract | Introduction 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. | ||||
| Learning objective | Learn 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. | ||||
| Content | A 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 / Notice | Agreement with one of the responsible Professors is necessary. | ||||