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n.BA.AD.IPRS.24HS (Image Processing for Remote Sensing) 
Module: Image Processing for Remote Sensing
This information was generated on: 07 November 2025
No.
n.BA.AD.IPRS.24HS
Title
Image Processing for Remote Sensing
Credits
2

Description

Version: 2.0 start 01 August 2025

 

Study Programme Applied Digital Life Sciences
Regulations Applicable RPO, 29 January 2008, School of Life Sciences and Facility Management Academic Regulations, 15 Dec. 2009, Annex for the Bachelor of Applied Digital Life Sciences degree programme
Module Type  
  Compulsory Module  X Elective Module    Optional Module
Planned Semester 5th Semester
Module Coordinator Johann Junghardt
Telephone / E-Mail +41 (0)58 934 50 13 / johann.junghardt@zhaw.ch
Lecturer(s),
Speaker(s),
Associate(s)		 Johann Junghardt and various internal lecturers
Entrance Requirements Environmental Systems 1, Remote Sensing and Geodata Acquisition
Learning Outcomes and Competencies Technical skills:
The students:
  • develop an applied expertise in remote sensing sensor systems and data and products
  • will execute their individual acquisition and processing of open and free remote sensing data, databases and data management.
  • use different IT infrastructures for managing big remote sensing data volumes, online databases and cloud computing (Google Earth Engine)
  • apply image classification and automatisation processes in image processing in GIS/Python and R.
  • have insight in Landcover and Landuse classification algorithms and approaches based on remote sensing products and other imaging products from different fields of application.
  • are introduced to working with RADAR, LiDAR and multispectral/hyperspectral data.
Transferable skills:
The students:
  • work on their own holistic projects guided by the content of the module.
  • have to navigate, address and judge different datasets and their properties.
  • are familiar with coding in different languages.
  • are introduced to different kinds of data quality and data origin and have to assess and choose their own sample data.
Module Content
  • Remote sensing methods and datasets
  • Types and Application of Remote Sensing Products
  • Digital image processing and interpretation of remote sensing data
  • Image classification, Landuse and Lancover classification approaches
  • Image Processing and analysis with GIS/QGIS/R/Python
  • Processing and Analysis of RADAR, LiDAR, Multipectral and Hyperspectral data.
  • Data Quality and Uncertainty ·
  • Remote sensing and artificial intelligence…
Follow-up Modules -
Methods of Instruction 
  • Paper club
  • Individual and group assignments (most of the exercises will be held in collaborative and mixed forms or digital communication and collaboration).
  • Applied field work (drone remote sensing)
  • Input by external experts from the swiss remote sensing ecosystem
Digital Resources Example:
  • Videomanuals
  • Screencasts for exercises and solutions
  • Academic papers on specific content
  • Practical Exerises in Google Earth Engine, QGIS and R
Lesson Structure / Workload  
 Contact Hours 28
 Guided Self-Study 14
 Independent Self-Study 18
 Total Workload 60
Classroom Attendance Attendance is encouraged, but not enforced with attendance list
Assessment Experience grade (100%):
Language of Instruction  English
Comments -

 

Note

Course: Image Processing for Remote Sensing
No.
n.BA.AD.IPRS.24HS.V
Title
Image Processing for Remote Sensing

Note

  • No module description is available in the system for the cut-off date of 07 November 2025.