Silvan Leinss: Catalogue data in Spring Semester 2020

Name Dr. Silvan Leinss
DepartmentCivil, Environmental and Geomatic Engineering
RelationshipLecturer

NumberTitleECTSHoursLecturers
101-0510-00LFirst Year Project Work Restricted registration - show details 3 credits6AD. Braun, M. Floriancic, S. Leinss, E. Morgenroth, J. Wang
AbstractProject on a topic pertaining to Environmental Engineering or Geomatics and Planning. The project work is carried out in a team.
ObjectiveThe project work not only tests efficiency in team- and project management, but also how to develop clearly structured, interdisciplinary concept solutions.
ContentStudents can choose from different subjects and tasks.
102-0528-01LExperimental and Computer Laboratory (Year Course) Information Restricted registration - show details 10 credits2PD. Braun, M. Giuliani, M. Haupt, M. Holzner, J. Jimenez-Martinez, S. Leinss, M. Magdali, M. Maurer, J. Wang, Z. Wang, M. Willmann
AbstractIn the Experimental and Computer Laboratory students are introduced to research and good scientific practice. Experiments are conducted in different disciplines of environmental engineering. Data collected during experiments are compared to the corresponding numeric simulations. The results are documented in reports or presentations.
ObjectiveThe student will learn the following skills: basic scientific work, planning and conducting scientific experiments, uncertainty estimations of measurements, applied numerical simulations, modern sensor technology, writing reports.
ContentThe Experimental and Computer Laboratory is building on courses in the corresponding modules. Material from these courses is a prerequisite or co-requisite (as specified below) for participating in the Experimental and Computer Laboratory (MODULE: Project in the Experimental and Computer Laboratory):
- WatInfra: Water Network Management
- UWM: SysUWM + ProcUWM: Operation of Lab-WWTP
- AIR: Air Quality Measurements
- WasteBio: Anaerobic Digestion
- WasteRec: Plastic Recycling
- ESD: Environmental Assessment
- GROUND: Groundwater Field Course Kappelen
- WRM: Modelling Optimal Water Allocation
- FLOW: 1D Open Channel Flow Modelling
- LAND: Landscape Planning and Environmental Systems
- RIVER: Discharge Measurements
- HydEngr: Hydraulic Experiments
- RemSens: Earth Observation and Landscape Planning
- SOIL: Soil and Environmental Measurements Lab
Lecture notesWritten material will be available.
102-0617-01LMethodologies for Image Processing of Remote Sensing Data3 credits2GI. Hajnsek, O. Frey, S. Leinss
AbstractThe 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.
ObjectiveThe 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.
ContentEach 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 notesHandouts for each topic will be provided.
LiteratureSuggested 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