Basic information
- Field of study
- Geospatial Computer Science
- Major
- Remote Sensing and GIS
- Organisational unit
- Faculty of Geo-Data Science, Geodesy, and Environmental Engineering
- Study level
- Second-cycle (engineer) programme
- Form of study
- Full-time studies
- Profile
- General academic
- Didactic cycle
- 2024/2025
- Course code
- DGEITGS.IIi1.07197.24
- Lecture languages
- English
- Mandatoriness
- Obligatory
- Block
- Major Modules
- Course related to scientific research
- Yes
|
Period
Semester 1
|
Method of verification of the learning outcomes
Exam
Activities and hours
Lectures:
15
Project classes: 30 |
Number of ECTS credits
4
|
Goals
| C1 | Familiarizing students with the fundamental principles of observation platforms and sensors used in remote sensing, including their classification, characteristics, and applications. |
| C2 | Preparing students to effectively apply remote sensing technologies in environmental monitoring, mapping, and spatial analyses across various fields of science and professional practice. |
Course's learning outcomes
| Code | Outcomes in terms of | Learning outcomes prescribed to a field of study | Methods of verification |
| Knowledge – Student knows and understands: | |||
| W1 | applied digital image processing methods | GEI2A_W03 | Participation in a discussion |
| W2 | electromagnetic radiation and basic physical quantities that can be determined by remote sensing | GEI2A_W01, GEI2A_W03 | Examination |
| Skills – Student can: | |||
| U1 | perform basic operations on digital images including: digital image filtering with appropriate algorithms and lossy and lossless compression | GEI2A_U02, GEI2A_U05 | Project |
| Social competences – Student is ready to: | |||
| K1 | fluently communicate in stressful business situations, expresses clearly own point of view during presentations and negotiation | GEI2A_K01, GEI2A_K04 | Presentation |
Student workload
| Activity form | Average amount of hours* needed to complete each activity form | |
| Lectures | 15 | |
| Project classes | 30 | |
| Realization of independently performed tasks | 50 | |
| Contact hours | 5 | |
| Student workload |
Hours
100
|
|
| Workload involving teacher |
Hours
45
|
|
* hour means 45 minutes
Program content
| No. | Program content | Course's learning outcomes | Activities |
| 1. |
Introduction to observation platforms - overview, types, and applications in remote sensing. |
W1, W2 | Lectures |
| 2. |
Remote sensing sensors - classification, characteristics, operating principles, technologies, data analysis methods, applications in remote sensing, applications in monitoring and mapping. |
W1, W2, U1, K1 | Project classes |
Extended information/Additional elements
Teaching methods and techniques :
Discussion, Lectures, Design thinking, E-learning, Problem Based Learning, Project Based Learning, Case study, Workshop, Team Based Learning
| Activities | Methods of verification | Credit conditions |
|---|---|---|
| Lectures | Participation in a discussion, Examination | |
| Project classes | Project, Presentation |
Conditions and the manner of completing each form of classes, including the rules of making retakes, as well as the conditions for admission to the exam
To be allowed to take the exam, all projects must be passed.
Method of determining the final grade
Evaluation is subject to the manner of execution and presentation of project.
Manner and mode of making up for the backlog caused by a student justified absence from classes
Compensating for the backlog caused by absence: depending on the classes subject – self-realisation of excercises with the help of individual consultations with the instructor.
Prerequisites and additional requirements
Students should have a basic knowledge of remote sensing and the use of Geographic Information Systems (GIS). Proficiency in English at a level enabling work with scientific literature is required.
Rules of participation in given classes, indicating whether student presence at the lecture is obligatory
Lectures: students participate in the class by learning the subsequent teaching content according to the course syllabus. Students should ask questions and clarify doubts on an ongoing basis. Audio-visual recording of the lecture requires the consent of the instructor. Project classes: students perform in teams projects aimed at achieving the competencies assumed by the syllabus.
Literature
Obligatory- 1. Paul M. Mather, Magaly Koch, 2011. Computer Processing of Remotely‐Sensed Images: An Introduction. John Wiley & Sons, Ltd
- 2. Robert A. Schowengerdt, 2012. Remote Sensing, Third Edition: Models and Methods for Image Processing. Elsevier.
- 3. Emilio Chuvieco, 2016, Fundamentals of Satellite Remote Sensing: An Environmental Approach. CRC Press.
Scientific research and publications
Publications- Michałowska, K.; Pirowski, T.; Głowienka, E.; Szypuła, B.; Malinverni, E.S. Sustainable Monitoring of Mining Activities: Decision-Making Model Using Spectral Indexes. Remote Sens. 2024, 16, 388. https://doi.org/10.3390/rs16020388
- Hejmanowska, B.; Kramarczyk, P.; Głowienka, E.; Mikrut, S. Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images. Remote Sens. 2021, 13, 3176. https://doi.org/10.3390/rs13163176
- Głowienka, E; Zembol, N. Forest Community Mapping Using Hyperspectral (CHRIS/PROBA) and Sentinel-2 Multispectral Images. Geomatics and Environmental Engineering 2022, 16, 103 -117.