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.IIi1.07198.24
- 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 | To introduce the students to the principles of visual and computer-aided interpretation of aerial photographs and remotely sensed data, especially for environmental applications |
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 | knows principles of visual and computer-aided interpretation of airborn and satellite images and ALS data | GEI2A_W01, GEI2A_W03 | Examination |
| W2 | knows computer techniques for remote sensing image enhancement and information extraction | GEI2A_W03 | Examination |
| Skills – Student can: | |||
| U1 | has developed skills of visual and computer-aided interpretation of airborn and satellite images and ALS data for different purposes | GEI2A_U01, GEI2A_U03, GEI2A_U09 | Activity during classes, Execution of exercises, Project, Examination |
| U2 | use computer techniques for remote sensing image enhancement and information extraction, to facilitate the interpretation of airborn and satellite images and ALS data | GEI2A_U01, GEI2A_U03, GEI2A_U09 | Activity during classes, Execution of exercises, Project, Examination |
| Social competences – Student is ready to: | |||
| K1 | responsible usage of remotely sensed data in practical applications | GEI2A_K01 | Activity during classes, Project, Examination |
Program content ensuring the achievement of the learning outcomes prescribed to the module
Student workload
| Activity form | Average amount of hours* needed to complete each activity form | |
| Lectures | 15 | |
| Project classes | 30 | |
| Preparation for classes | 15 | |
| Realization of independently performed tasks | 10 | |
| Examination or final test/colloquium | 2 | |
| Contact hours | 2 | |
| Preparation of project, presentation, essay, report | 40 | |
| Student workload |
Hours
114
|
|
| Workload involving teacher |
Hours
45
|
|
* hour means 45 minutes
Program content
| No. | Program content | Course's learning outcomes | Activities |
| 1. |
1) Basic techniques for image enhancement and information extraction from remotely sensed data. |
U1, U2, K1 | Project classes |
| 2. |
Principles of visual interpretation of aerial photographs and satellite imagery. Principles of application-oriented interpretation of ALS data. Computer techniques for image enhancement and information extraction. Land use/land cover (LU/LC) interpretation and mapping. Vegetation interpretation and mapping. Interpretation and mapping of surface waters and hydrology. Cultural heritage oriented applications. Interpretation of soils and geological features. Multitemporal analyses. |
W1, W2, K1 | Lectures |
Extended information/Additional elements
Teaching methods and techniques :
Demonstration, Workshop, Project Based Learning, E-learning, Group work, Lectures
| Activities | Methods of verification | Credit conditions |
|---|---|---|
| Lectures | Activity during classes, Project, Examination | |
| Project classes | Activity during classes, Execution of exercises, Project, Examination |
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
The basis for passing the exercises is active participation in classes and positive results of the current checking whether the assumed learning outcomes have been achieved by the student. To pass the exercises it is necessary to complet all project tasks and present appriopriate reports or/and presentations. In the case of project tasks, the following will be evaluated: the correctness of the adopted methodology of solving the task, its final effect, the timeliness of execution and the way of presenting the results, as well as answers to questions asked by the lecturer during the presentation of results. The grade from the exercises will be the arithmetic mean of the grades from individual projects. Additional bonus may be granted based on activity and participation in discussion during classes. To pass the subject, it is required to obtain positive grades (minimum 3.0) from each of the projects carried out. The student who participated in compulsory classes (i.e. missed no more than 2 classes without excuse) is eligible for two additional approaches to pass the project or the passing test.
Method of determining the final grade
Final grade = 0.5*project grade + 0.5* exam grade Both, project and exam, has to be graded at least 3.0
Manner and mode of making up for the backlog caused by a student justified absence from classes
The conditions of making up for the backlog resulting from the student's absence will be determined in an individual manner based on: the number of absences, the type of the backlog and the degree of advancement of the student in the performance of his exercises. A way to compensate for the backlog may be to take exercise classes in another exercise group (after prior notification and with the consent of the lecturer) or the student's own work with the possibility of consulting it with lecturers.
Prerequisites and additional requirements
Prerequisites: Basic knowledge of remote sensing imaging techniques and methods of remotely sensed data processing.
Rules of participation in given classes, indicating whether student presence at the lecture is obligatory
Participation in the excersisse classes is mandatory. A maximum of 2 (in words: two) unjustified absences is allowed in the semester. The excuse for absence may be health reasons or other important reasons recognized by the instructor. The student is obliged to justify absence from the first classes after the reason for the absence has ceased. Exceeding the threshold of 20% of unjustified absence results in the inability to pass the exercises. In exceptional cases, a student who has important random causes or due to a documented, long-term illness exceeded the above-mentioned limits, can obtain the consent of the teacher to pass the exercises.
Literature
Obligatory- Thomas Lillesand, Ralph W. Kiefer, Jonathan Chipman: Remote Sensing and Image Interpretation, 7th Edition. Wiley 2015
- John R. Jensen: Remote Sensing of the Environment: An Earth Resource Perspective, 2nd Edition. Pearson 2007.
Scientific research and publications
Publications- Sebastian Aleksandrowicz, Anna Wawrzaszek, Wojciech DRZEWIECKI, Michał Krupiński: Change detection using global and local multifractal description. IEEE Geoscience and Remote Sensing Letters 2016 vol. 13 no. 8, s. 1183–1187
- Dorota Maryniak, Wojciech DRZEWIECKI: Zmiany pokrycia terenu Pustyni Błędowskiej w latach 1926–2005 — Land cover changes in Błędowska Desert area between 1926 and 2005.Archiwum Fotogrametrii, Kartografii i Teledetekcji, 2010 vol. 21, s. 245–256
- Wojciech DRZEWIECKI: Sub-pixel classification of middle-resolution satellite images – evaluation of regression trees applicability to monitor impervious surfaces coverage. Geomatics and Environmental Engineering, 2010 vol. 4 no. 4, s. 61–75
- Wojciech DRZEWIECKI: Monitoring zmian pokrycia i użytkowania terenu na podstawie wieloczasowych obrazów teledetekcyjnych — Land-use/land cover monitoring based on multitemporal remote sensing images.Roczniki Geomatyki 2008 t. 6 z. 3 spec., s. 131–142.
- Stanisław MULARZ, Wojciech DRZEWIECKI: Interpretacja głównych elementów krajobrazu na teledetekcyjnych obrazach lotniczych i satelitarnych — Interpretation of main landscape elements on aerial and satellite photographs . Czasopismo Techniczne, R. 105 z. 4. Architektura , 2008 , s. 101–107
- Mierzwa W., Rzonca A., 2003 - Skanowanie powierzchni jako nowa metoda rejestracji i interpretacji szczegółów architektonicznych (Surface scanning as a new method of recording and interpretations of architecture details), Archiwum Fotogrametrii, Kartografii i Teledetekcji vol. 13B, Wrocław 2003
- Gabor K., Rzonca A., 2014 - Development of a system for monitoring of technical condition of a historical site on the example of barracks in the former Auschwitz-Birkenau camp - Opracowanie systemu monitoringu obrazowego stanu technicznego obiektu zabytkowego na przykładzie baraków byłego obozu Auschwitz-Birkenau. Pomiary, Automatyka, Kontrola. 2014 vol. 60 nr 2, s. 122-125
- Majek K., Rzonca A., 2016 - Lidarometry as a Variant of Integration of Photogrammetric and Laser Scanning Data Lidarometria jako wariant integracji danych fotogrametrycznych oraz skaningowych, MAM 2016 nr 08, s. 268-273
- Tomasz PIROWSKI, Bartłomiej Szypuła, Michał Marciak: Interpretation of multispectral satellite data as a tool for 10. 11. 12. detecting archaeological artifacts (Navkur Plain and Karamleis Plain, Iraq). Archaeological and Anthropological Sciences, 2022 — vol. 14 iss. 9 art. no. 166, s. 1–21.
- Tomasz PIROWSKI, Michał Marciak, Marcin Sobiech: Potentialities and limitations of research on VHRS data: Alexander the Great’s military camp at Gaugamela on the Navkur Plain in Kurdish Iraq as a test case. Remote Sensing, 2021 — vol. 13 iss. 5 art. no. 904, s. 1–31
- Krystyna Michałowska, Ewa GŁOWIENKA: Multi-temporal analysis of changes of the southern part of the Baltic Sea coast using aerial remote sensing data. Remote Sensing, 2022 — vol. 14 iss. 5 art. no. 1212, s. 1–17.
- Rafał GAWAŁKIEWICZ, Aleksandra WAGNER: Morphometric parameters of the water bodies of Bagry Wielkie and Bagry Małe in the biodiversity context — Parametryzacja morfometryczna zbiorników Bagry Wielkie i Bagry Małe na tle bioróżnorodności, Geoinformatica Polonica ; ISSN 1642-2511. — 2023 — vol. 22, s. 35–51. — Bibliogr. s. 50–51