Irena Hajnsek: Katalogdaten im Herbstsemester 2021

NameFrau Prof. Dr. Irena Hajnsek
LehrgebietErdbeobachtungen (Mikrowellen-Fernerkundung)
Adresse
Institut für Umweltingenieurwiss.
ETH Zürich, HCP G 33.3
Leopold-Ruzicka-Weg 4
8093 Zürich
SWITZERLAND
Telefon+41 44 633 74 55
E-Mailhajnsek@ifu.baug.ethz.ch
DepartementBau, Umwelt und Geomatik
BeziehungOrdentliche Professorin

NummerTitelECTSUmfangDozierende
101-0522-10LDoctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering Belegung eingeschränkt - Details anzeigen
Findet dieses Semester nicht statt.
Number of participants limited to 21.
1 KP2SB. Soja, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, K. Schindler
KurzbeschreibungCurrent 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.
Lernziel- 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
InhaltCurrent research at D-BAUG will be presented and discussed.
Voraussetzungen / BesonderesThis 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.
101-0523-12LFrontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering (HS21) Belegung eingeschränkt - Details anzeigen
Number of participants limited to 21.
1 KP2SM. A. Kraus, E. Chatzi, F. Corman, O. Fink, I. Hajnsek, M. Lukovic, K. Schindler, B. Soja, B. Sudret, M. J. Van Strien
KurzbeschreibungThis 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).
LernzielStudents 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.
InhaltWith 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.
Voraussetzungen / BesonderesThis 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.
102-0515-01LSeminar Umweltingenieurwissenschaften Information Belegung eingeschränkt - Details anzeigen 3 KP3SE. Secchi, P. Burlando, I. Hajnsek, M. Maurer, P. Molnar, E. Morgenroth, S. Pfister, S. Sinclair, R. Stocker, J. Wang
KurzbeschreibungDie Kurs ist in Form eines Seminars mit studentischen Vorträgen organisiert. Themen aus den Kerndisziplinen des Studiengangs (Wasserressourcen und -haushalt, Siedlungswasserwirtschaft, Stoffhaushalt, Entsorgungstechnik, Luftreinhaltung, Erdbeobachtung) werden diskutiert auf der Basis von wissenschaftlichen Veröffentlichungen, die von den Studierenden dargestellt und kritisch begutachtet werden.
LernzielNeue Forschungsergebnisse und Anwendungsbeispiele aus dem Fachbereich der Umweltingenieurwissenschaften kennen und analysieren lernen.
102-0617-00LBasics and Principles of Radar Remote Sensing for Environmental Applications3 KP2GI. Hajnsek
KurzbeschreibungThe course will provide the basics and principles of Radar Remote Sensing (specifically Synthetic Aperture Radar (SAR)) and its imaging techniques for the use of environmental parameter estimation.
LernzielThe course should provide an understanding of SAR techniques and the use of the imaging tools for bio/geophysical parameter estimation. At the end of the course the student has the understanding of
1. SAR basics and principles,
2. SAR polarimetry,
3. SAR interferometry and
4. environmental parameter estimation from multi-parametric SAR data
InhaltThe course is giving an introduction into SAR techniques, the interpretation of SAR imaging responses and the use of SAR for different environmental applications. The outline of the course is the following:
1. Introduction into SAR basics and principles
2. Introduction into electromagnetic wave theory
3. Introduction into scattering theory and decomposition techniques
4. Introduction into SAR interferometry
5. Introduction into polarimetric SAR interferometry
6. Introduction into bio/geophysical parameter estimation (classification/segmentation, soil moisture estimation, earth quake and volcano monitoring, forest height inversion, wood biomass estimation etc.)
SkriptHandouts for each topic will be provided
LiteraturFirst readings for the course:
Woodhouse, I. H., Introduction into Microwave Remote Sensing, CRC Press, Taylor & Francis Group, 2006.
Lee, J.-S., Pottier, E., Polarimetric Radar Imaging: From Basics to Applications, CRC Press, Taylor & Francis Group, 2009.
Complete literature listing will be provided during the course.
102-0675-AALEarth Observation
Belegung ist NUR erlaubt für MSc Studierende, die diese Lerneinheit als Auflagenfach verfügt haben.

Alle andere Studierenden (u.a. auch Mobilitätsstudierende, Doktorierende) können diese Lerneinheit NICHT belegen.
4 KP9RI. Hajnsek
KurzbeschreibungThe aim of the course is to provide the fundamental knowledge about earth observation sensors, techniques and methods for bio/geophysical environmental parameter estimation.
Lernziel
102-0675-00LErdbeobachtung4 KP3GI. Hajnsek, E. Baltsavias
KurzbeschreibungDas Ziel der Lehrveranstalltung ist die Vermittlung von Grundlagen über Erdbeobachtungs-Sensoren, Techniken und Methoden zur Bestimmung von bio-/geo-physikalischen Umweltparametern.
LernzielDie Lehrveranstalltung sollte Grundlagen und einen Überblick über derzeitige und zukünftige Erdbeobachtungssensoren und deren Einsatz zur Umweltparameterbestimmung vermitteln. Die Studenten sollten am Ende der Veranstalltung Wissen über
1. Grundlagen zum Messprinzip
2. Grundlagen in der Bildaufnahme
3. Grundlagen zu den sensorspezifischen Geometrien
4. Sensorspezifische Bestimmung von Umweltparametern
erworben haben.
InhaltDie Lehrveranstaltung gibt einen Einblick in die heutige Erdbeoachtung mit dem follgenden skizzierten Inhalt:
1. Einführung in die Fernerkundung von Luft- und Weltraum gestützen Systemen
2. Einführung in das Elektromagnetische Spektrum
3. Einführung in optische Systeme (optisch und hyperspektral)
4. Einführung in Mikrowellen-Technik (aktiv und passiv)
5. Einführung in atmosphärische Systeme (meteo und chemisch)
6. Einführung in die Techniken und Methoden zur Bestimmung von Umweltparametern
7. Einführung in die Anwendungen zur Bestimmung von Umweltparametern in der Hydrologie, Glaziologie, Forst und Landwirtschaft, Geologie und Topographie
SkriptFolien zu jeden Vorlesungsblock werden zur Verfügung gestellt.
LiteraturAusgewählte Literatur wird am Anfang der Vorlesung vorgestellt.