Irena Hajnsek: Catalogue data in Autumn Semester 2023 |
| 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 | ||||||||||||||||||||||||||||||||||||||||||||||||||
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| 101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering  | 1 credit | 1S | V. Ntertimanis, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| 101-0523-14L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering | 1 credit | 1G | V. Ntertimanis, E. Chatzi, F. Corman, I. Hajnsek, M. A. Kraus, M. Lukovic, 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| 102-0004-00L | Introduction into Environmental Engineering | 3 credits | 2G | P. Molnar, R. Boes, P. Burlando, I. Hajnsek, S. Hellweg, M. Maurer, E. Morgenroth, R. Stocker, J. Wang | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this course students are introduced to how environmental problems in the areas of water quantity and quality, waste production and recycling, air pollution control, are formulated and solved with engineering methods. The course makes a connection between the theoretical Bachelor foundation classes and practical topics of environmental engineering in six main thematic areas. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | After completing this course, the student will be able to: - formulate key global environmental problems - develop a systems perspective and solutions to the problems (critical thinking) - identify and solve simple numerical problems in the domain areas - understand why/how we use data/models in environmental engineering - develop own interest in the domain areas and see career opportunities | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Topics of study: 0. Introduction – description of the Earth System, main stressors, global warming, introduction into the methods and goals of environmental engineering. 1. Water Science & Engineering – definition of the global water cycle and hydrological regimes, surface/subsurface flow equations (advection, diffusion), water resources management, climate change. 2. Resource Management & Recovery – waste management, recycling, resource recovery, lifecycle assessment, water and carbon footprints. 3. Urban Water Technology – water quality parameters, municipal water and wastewater treatment processes and technologies, urban water systems (infrastructure). 4. River and Hydraulic Engineering – hydropower production, flood protection, river restoration, ecological measures at hydropower plants. 5. Air Quality – air quality parameters, main air pollutants, air quality in cities/indoor, emission control, the plume dispersion model. 6. Earth Observation – satellite observation of the Earth System from space, methods, environmental applications (glaciers, forest, land surface change) | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Course will take place in English and German (bilingual). The English textbook by Masters and Ela (see below) will be complemented by instructors materials to the individual thematic topics. Lecture presentations will be the main study material. There is no formal Script. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | - Masters, G.M., & Ela, W.P. (2014). Introduction to Environmental Engineering and Science, Third Edition, Prentice Hall, 692 pp, https://ebookcentral.proquest.com/lib/ethz/reader.action?docID=5831826 - lecture presentations and selected papers | |||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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| 102-0515-01L | Environmental Engineering Seminars | 3 credits | 3S | S. Sinclair, P. Burlando, I. Hajnsek, S. Hellweg, M. Maurer, P. Molnar, E. Morgenroth, C. Oberschelp, S. Pfister, E. Secchi, R. Stocker, J. Wang | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course is organized in the form of seminars held by the students. Topics selected from the core disciplines of the curriculum (water resources, urban water engineering, material fluxes, waste technology, air polution, earth observation) are discussed in the class on the basis of scientific papers that are illustrated and critically reviewed by the students. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Learn about recent research results in environmental engineering and analyse practical applications in environmental engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 102-0617-00L | Basics and Principles of Radar Remote Sensing for Environmental Applications | 3 credits | 2G | I. Hajnsek | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The 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.) | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Handouts for each topic will be provided | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | First 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-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 | The aim of the course is to provide the fundamental knowledge about earth observation sensors, techniques and methods for bio/geophysical environmental parameter estimation. Students should know at the end of the course: 1. Basics of measurement principle 2. Fundamentals of image acquisition 3. Basics of the sensor-specific geometries 4. Sensor-specific determination of environmental parameters | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| 102-0675-00L | Earth Observation | 4 credits | 3G | I. Hajnsek, P. Bernhard | ||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 | The aim of the course is to provide the fundamental knowledge about earth observation sensors, techniques and methods for bio/geophysical environmental parameter estimation. Students should know at the end of the course: 1. Basics of measurement principle 2. Fundamentals of image acquisition 3. Basics of the sensor-specific geometries 4. Sensor-specific determination of environmental parameters | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Die 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Folien zu jeden Vorlesungsblock werden zur Verfügung gestellt. | |||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Ausgewählte Literatur wird am Anfang der Vorlesung vorgestellt. | |||||||||||||||||||||||||||||||||||||||||||||||||||||