Basic information
- Field of study
- Geodesy, Surveying and Cartography
- Major
- Geoinformation, Photogrammetry and Remote Sensing
- 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
- 2023/2024
- Course code
- DGIKGFS.IIi4.05942.23
- Lecture languages
- English
- Mandatoriness
- Elective
- Block
- Elective Modules in Foreign Language
- Course related to scientific research
- Yes
|
Period
Semester 3
|
Method of verification of the learning outcomes
Completing the classes
Activities and hours
Project classes:
30
|
Number of ECTS credits
3
|
Goals
| G1 | Course aims for student to acquire practical knowledge necessary for using Python language in processing data accumulated with remote sensing methods and learn how to interface self-developed applications with existing tools. |
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 | principles of programming in the Python language | GIK2A_W05, GIK2A_W06 | Activity during classes, Execution of exercises, Execution of a project |
| Skills – Student can: | |||
| U1 | to create programs in the Python language | GIK2A_U11 | Activity during classes, Execution of exercises, Execution of a project |
| U2 | to extend the functionality of geoinformatics tools using the Python language | GIK2A_U06, GIK2A_U11 | Activity during classes, Execution of exercises, Execution of a project |
| Social competences – Student is ready to: | |||
| K1 | creativity in approaching programming | GIK2A_K01 | Activity during classes, Execution of a project |
Program content ensuring the achievement of the learning outcomes prescribed to the module
The module allows for familiarizing oneself with the principles of programming in the Python language, utilizing its selected libraries, and designing and practically implementing a graphical interface. It then teaches how to apply the acquired principles to create a graphical application using object-oriented programming.
Student workload
| Activity form | Average amount of hours* needed to complete each activity form | |
| Project classes | 30 | |
| Preparation for classes | 30 | |
| Realization of independently performed tasks | 10 | |
| Examination or final test/colloquium | 2 | |
| Contact hours | 2 | |
| Preparation of project, presentation, essay, report | 15 | |
| Student workload |
Hours
89
|
|
| Workload involving teacher |
Hours
30
|
|
* hour means 45 minutes
Program content
| No. | Program content | Course's learning outcomes | Activities |
| 1. |
Project exercises 1. Introduction to the Python Language Getting acquainted with the language interpreter. Comparison with other programming languages. Variable assignments, mathematical and logical operators. Basic data types and their representation. Keywords and fundamental language concepts. Complex structures. Understanding immutable objects. 2. Scripts and Program Flow Control Formatting correct scripts and executing them. Conditional and looping structures, function definitions controlling program flow. Differences between an interpreter and a compiler. Handling exceptions in a program. Input/output handling for different devices. Defining functions of different types. 3. Classes, Objects, and Modules Introduction to object-oriented programming in Python. Building classes, objects, and methods, their inheritance and polymorphism. Ways to modularize scripts and import libraries. 4. Selected Language Libraries Discussion of important elements of standard libraries. Libraries containing geospatial transformations: GDAL, OGR, OSR. Methods for reading, writing, and processing raster and vector data. 5. QT Interface Design Utilizing tools for rapid application development (RAD), automatic code generation, connecting interface elements with executable code. Introduction to the concept of event-driven programming. Familiarization with libraries for building graphical interfaces. 6. Practical Implementations Sample applications using Python and QT. Implementation of photogrammetric and remote sensing algorithms for data processing. Manipulating images through direct I/O and libraries. Data visualization methods
Selected topics may require webinar (online) atenndance. |
W1, U1, U2, K1 | Project classes |
Extended information/Additional elements
Teaching methods and techniques :
webinars and online environment for e-learning: UPEL, BigBlueButton, Clickmeeting or MS Teams., E-learning
| Activities | Methods of verification | Credit conditions |
|---|---|---|
| Project classes | Activity during classes, Execution of exercises, Execution of a project |
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
Design exercises are passed on the basis of a colloquium. In the absence of a pass, there will be an opportunity to improve.
Method of determining the final grade
Evaluation of the student's activity on exercises, assignments and the final project.
Manner and mode of making up for the backlog caused by a student justified absence from classes
The student makes up the arrears on his own.
Prerequisites and additional requirements
General x86 computer usage
Text editor familiarity
Fluent common english language
Rules of participation in given classes, indicating whether student presence at the lecture is obligatory
Project exercises: Students carry out practical work aimed at obtaining the competences assumed by the syllabus. The method of project implementation and the final effect are assessed.
Literature
Obligatory- Python documentation: https://docs.python.org/3/
- Qt documentation: https://doc.qt.io/qtforpython-5/contents.html
- Summerfield Mark: “Rapid GUI Programming with Python and Qt”. Prentice Hall 2008
- Dawson Michael: “Python dla każdego.Podstawy programowania”. Helion 2014
Scientific research and publications
Publications- K. Pyka, M. Twardowski: "Miejsce wolnego oprogramowania w nauczaniu geoinformatyki". Archiwum Fotogrametrii, Kartografii i Teledetekcji. 2007.
- Hejmanowska, B., Twardowski, M., & Żądło, A. (2021). An Application of the “Traffic Lights” Idea to Crop Control in Integrated Administration Control System. Geomatics and Environmental Engineering, 15(4), 129–152. https://doi.org/10.7494/geom.2021.15.4.129
- Rzonca A., Twardowski M., 2022, The lidargrammetric model deformation method for altimetric UAV-ALS data enhancement