Irena Hajnsek: Catalogue data in Spring Semester 2022 |
| Name | Prof. Dr. Irena Hajnsek |
| Field | Earth Observation (Microwave Remote Sensing) |
| Address | Institut für Umweltingenieurwiss. ETH Zürich, HIF D 89.2 Laura-Hezner-Weg 7 8093 Zürich SWITZERLAND |
| Telephone | +41 44 633 74 55 |
| hajnsek@ifu.baug.ethz.ch | |
| 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  Does not take place this semester. Number of participants limited to 21. | 1 credit | 2S | B. Soja, E. Chatzi, F. Corman, I. Hajnsek, K. Schindler | |
| 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. | ||||
| 102-0617-01L | Methodologies for Image Processing of Remote Sensing Data | 3 credits | 2G | I. Hajnsek, O. Frey, S. Li | |
| Abstract | The aim of this course is to get an overview of several methodologies/algorithms for analysis of different sensor specific information products. It is focused at students that like to deepen their knowledge and understanding of remote sensing for environmental applications. | ||||
| Learning objective | The course is divided into two main parts, starting with a brief introduction to remote sensing imaging (4 lectures), and is followed by an introduction to different methodologies (8 lectures) for the quantitative estimation of bio-/geo-physical parameters. The main idea is to deepen the knowledge in remote sensing tools in order to be able to understand the information products, with respect to quality and accuracy. | ||||
| Content | Each lecture will be composed of two parts: Theory: During the first hour, we go trough the main concepts needed to understand the specific algorithm. Practice: During the second hour, the student will test/develop the actual algorithm over some real datasets using Matlab. The student will not be asked to write all the code from scratch (especially during the first lectures), but we will provide some script with missing parts or pseudo-code. However, in the later lectures the student is supposed to build up some working libraries. | ||||
| Lecture notes | Handouts for each topic will be provided. | ||||
| Literature | Suggested readings: T. M. Lillesand, R.W. Kiefer, J.W. Chipman, Remote Sensing and Image Interpretation, John Wiley & Sons Verlag, 2008 J. R. Jensen, Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall Series in Geograpic Information Science, 2000 | ||||
| 102-0675-AAL | Earth Observation 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 | I. Hajnsek | |
| Abstract | The aim of the course is to provide the fundamental knowledge about earth observation sensors, techniques and methods for bio/geophysical environmental parameter estimation. | ||||
| Learning objective | |||||