Martin Raubal: Catalogue data in Autumn Semester 2020 |
| Name | Prof. Dr. Martin Raubal |
| Field | Geoinformation Engineering |
| Address | Inst. f. Kartografie u. Geoinform. ETH Zürich, HIL G 37.3 Stefano-Franscini-Platz 5 8093 Zürich SWITZERLAND |
| Telephone | +41 44 633 30 26 |
| mraubal@ethz.ch | |
| URL | http://www.raubal.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  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. | ||||
| 103-0233-10L | Fundamentals of GIS | 6 credits | 5G | M. Raubal | |
| Abstract | Fundamentals of geographic information systems: spatial data modeling; metrics & topology; vector, raster and network data; thematic data; spatial statistics; system architectures; data quality; spatial queries and analysis; geovisualisation; spatial databases; group project with GIS software | ||||
| Learning objective | Knowing theoretical aspects of geographic information regarding data acquisition, representation, analysis and visualisation. Knowing the fundamentals of geoinformation technologies for the realization, application and operation of geographic information systems in engineering projects. | ||||
| Content | - Einführung GIS & GIScience - Konzeptionelles Modell & Datenschema - Vektorgeometrie & Topologie - Rastergeometrie und -algebra - Netzwerke - Thematische Daten - Räumliche Statistik - Systemarchitekturen & Interoperabilität - Datenqualität, Unsicherheiten & Metadaten - Räumliche Abfragen und Analysen - Präsentation raumbezogener Daten - Geodatenbanken | ||||
| Lecture notes | Vorlesungspräsentationen werden digital zur Verfügung gestellt. | ||||
| Literature | Bill, R. (2016). Grundlagen der Geo-Informationssysteme (6. Auflage): Wichmann. Bartelme, N. (2005). Geoinformatik - Modelle, Strukturen, Funktionen (4. Auflage). Berlin: Springer. | ||||
| 103-0234-AAL | GIS II 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. | 5 credits | 11R | M. Raubal | |
| Abstract | Advanced course in geoinformation technologies: conceptual and logical modelling of networks, 3D- and 4D-data and spatial processes in GIS; raster data structures and operations; mobile GIS; Internet and GIS; interoperability and data transfer; legal and technical foundations of spatial data infrastructures (SDI) | ||||
| Learning objective | Students will be able to carry out the following phases of a GIS project: data modelling, mobile data acquisition and analysis, Web publication of data and integration of interoperable geospatial web services into a Spatial Data Infrastructure (SDI). Students will deepen their knowledge of conceptual and logical modeling by means of the particular requirements of networks as well as 3D- and 4D-data. | ||||
| Literature | Worboys, M., & Duckham, M. (2004). GIS - A Computing Perspective (2nd Edition). Boca Raton, FL: CRC Press. Fu, P., Sun, J. (2010). Web GIS: Principles and Applications. Esri Press. | ||||
| 103-0237-00L | GIS III | 5 credits | 3G | M. Raubal | |
| Abstract | The course deals with advanced topics in GIS, such as Business aspects and Legal issues; Geostatistics; Human-Computer Interaction; Cognitive Issues in GIS; Geosensors; Spatial Data Mining and Machine Learning for GIS. | ||||
| Learning objective | Students will get a detailed overview of advanced GIS topics. They will work on a small project with geosensors in the lab and perform practical tasks relating to Geostatistics and Machine Learning. | ||||
| Lecture notes | Lecture slides will be made available in digital form. | ||||
| 103-0255-AAL | Geodata Analysis 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. | 2 credits | 4R | M. Raubal | |
| Abstract | The course deals with advanced methods in spatial data analysis. | ||||
| Learning objective | - Understanding the theoretical principles in spatial data analysis. - Understanding and using methods for spatial data analysis. - Detecting common sources of errors in spatial data analysis. - Advanced practical knowledge in using appropriate GIS-tools. | ||||
| Content | The course deals with advanced methods in spatial data analysis in theory as well as in practical exercises. | ||||
| Literature | MITCHELL, A., 2012, The Esri Guide to GIS Analysis - Modeling Suitability, Movement, and Interaction (3. Auflage), ESRI Press, Redlands, California | ||||
| 103-0717-AAL | Geoinformation Technologies and Analysis 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 | M. Raubal | |
| Abstract | Advanced geoinformation technologies and analyses methods: Mobile GIS; Web-GIS & Geo-Web-Services; Spatial Big Data; Temporal aspects in GIS; Analysis of movement data; User interfaces | ||||
| Learning objective | Knowing advanced topics of geoinformation technologies (Mobile GIS and Web-GIS) and spatio-temporal analysis methods for the realization, application and operation of Web-GIS in engineering projects. | ||||
| Prerequisites / Notice | Introductory GIS course | ||||
| 103-0717-00L | Geoinformation Technologies and Analysis | 6 credits | 5G | M. Raubal | |
| Abstract | Geoinformationstechnologien und -analysen für Fortgeschrittene: Mobile GIS; Web-GIS & Geo-Web-Services; Spatial Big Data; Zeitliche Aspekte in GIS; Analyse von Bewegungsdaten; Benutzerschnittstellen Übungen: Web-GIS-Semesterprojekt in Gruppenarbeit | ||||
| Learning objective | Fortgeschrittene Geoinformationstechnologien (Mobile GIS und Web-GIS) und raum-zeitliche Analysemethoden kennen, um Projekte im Zusammenhang mit Realisierung, Nutzung und Betrieb von Web-GIS ingenieurmässig planen und implementieren zu können. | ||||
| Content | - Mobile GIS - Web-GIS & Geo-Web-Services - Spatial Big Data - Zeitliche Aspekte in GIS - Analyse von Bewegungsdaten - Benutzerschnittstellen | ||||
| Lecture notes | Vorlesungspräsentationen werden digital zur Verfügung gestellt. | ||||
| Literature | Bill, R. (2016). Grundlagen der Geo-Informationssysteme (6. Auflage): Wichmann. Bartelme, N. (2005). Geoinformatik - Modelle, Strukturen, Funktionen (4. Auflage). Berlin: Springer. O'Sullivan, D., & Unwin, D. (2010). Geographic Information Analysis (2nd Edition). Wiley. | ||||
| Prerequisites / Notice | GIS GZ | ||||
| 103-0778-00L | GIS and Geoinformatics Lab | 4 credits | 3P | M. Raubal | |
| Abstract | Independent study project with novel geoinformation technologies. Information on past projects: http://gis-lab.ethz.ch/ | ||||
| Learning objective | This lab focuses on presenting spatial, temporal, and open data in tangible ways. Students will learn how to work with novel geoinformation technologies such as virtual/mixed reality or mobile applications. They will engage in teamwork, application design, programming and presenting their results. | ||||
| 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. | ||||
| 103-2233-AAL | GIS Basics 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 | M. Raubal | |
| Abstract | Fundamentals in geoinformation technologies: database principles, including modeling of spatial information, geometric and semantic models, topology and metrics; practical training with GIS software. | ||||
| Learning objective | Know the fundamentals in geoinformation technologies for the realization, application and operation of geographic information systems in engineering projects. | ||||
| Content | Modelling of spatial information Geometric and semantic models Topology & metrics Raster and vector models Databases Applications Labs with GIS software | ||||
| Literature | Worboys, M., & Duckham, M. (2004). GIS - A Computing Perspective (2nd ed.). Boca Raton, FL: CRC Press. O'Sullivan, D., & Unwin, D. (2010). Geographic Information Analysis (second ed.). Hoboken, New Jersey: Wiley. | ||||
| 166-0201-00L | Potential of Spatial Information- and Communication Technologies Does not take place this semester. Only for MAS in Future Transport Systems and CAS in Future Transport Systems: Technology Potential. | 3.5 credits | 3G | M. Raubal | |
| Abstract | The digital revolution, spatial information and communication systems in particular, have a significant influence on the development of new transport systems. Participants acquire an in-depth understanding of the functionality and application potential of spatial information systems and services and of communication technologies for deployment in future transport systems and applications. | ||||
| Learning objective | Familiarity with information and communication technologies (ICT) and spatial information technologies, and the ability to identify and utilise their potential to address concrete problems. | ||||
| Content | - Functionality and application of geographic information systems (GIS) to represent and analyse transport systems (acquire, model, analyse and visualise geodata) - Deployment potentials of GIS and ICT for efficient transport solutions (tangible, non-tangible) - Functionality and application of mobile spatial information technologies in future transport systems - Methods of spatiotemporal analysis and geodata analysis - Technical aspects of information and communication technologies (ICT) - Modelling, simulation and assessment of traffic behaviour - Basics of autonomous driving - Legal aspects of geodata - Applications: Traffic behaviour in Switzerland; location based services for energy-efficient behaviour; GIS for the Zurich traffic system (multimodal) | ||||
| Lecture notes | Distributed at start of module | ||||
| Literature | Distributed at start of module | ||||
| Prerequisites / Notice | Announced to students of the of the MAS / CAS at the beginning of the term. | ||||