Konrad Schindler: Catalogue data in Autumn Semester 2022 |
| 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 | 1S | M. J. Van Strien, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, V. Ntertimanis, K. Schindler, B. Soja | |
| 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. | ||||
| 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. | ||||
| 101-0523-13L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (HS22) | 1 credit | 1G | M. J. Van Strien, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, V. Ntertimanis, K. Schindler, B. Soja | |
| 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). | ||||
| 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. | ||||
| 103-0251-00L | Computational Methods for Geospatial Analysis | 4 credits | 4G | K. Schindler, J. A. Butt, B. Soja, Y. Xin | |
| Abstract | Introduction to mathematical and statistical tools for geospatial data analysis. | ||||
| Objective | The goal is to familiarise students with the principles and tools of geospatial data analysis, and to enable them to apply those tools to practical tasks. | ||||
| Content | The course introduces basic methods of geostatistics and geospatial data analysis. Topics include spatial correlation, auto-correlation and the variogram; surface interpolation (kernel-based, kriging, parametric surface models); spatially adaptive filtering (bilinear, guided filter); spatial stochastic processes and random fields; time series models and spatio-temporal analysis. | ||||
| 103-0287-00L | Image-based Mapping | 6 credits | 2G | K. Schindler | |
| Abstract | Application of photogrammetry and remote sensing methods for mapping and Earth observation. | ||||
| Objective | Learn how to apply photogrammetry, image analysis and machine learning to mapping tasks; hands-on experience in implementing automatic image analysis methods, and in judging their results. | ||||
| Content | Preprocessing 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 geometric and semantic computer vision and image analysis methods for mapping; assessment of prediction results | ||||
| Prerequisites / Notice | basic knowledge of photogrammetry, image processing and machine learning | ||||
| 103-0817-00L | Geomatics Seminar Does not take place this semester. | 4 credits | 2S | K. Schindler, K. W. Axhausen, A. Grêt-Regamey, L. Hurni, M. Raubal, B. Soja, 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. | ||||
| 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. | ||||
| 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. | ||||
| 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 | ||||