Display language
To modulepage Generate PDF

#41012 / #2

Seit SoSe 2023


Project Computer Vision for Remote Sensing


Demir, Begüm




Fakultät IV

Institut für Technische Informatik und Mikroelektronik

34342200 FG Remote Sensing Image Analysis

No information


EN 5

Demir, Begüm


Learning Outcomes

Participants of this project course gain practical experience in applying computer vision techniques to address Earth observation questions in a collaborative team and acquire knowledge on state-of-the-art topics in the field of computer vision for remote sensing.


Recent advances in satellite technology have led to a regular, frequent, and high-resolution monitoring of Earth at the global scale, providing an unprecedented amount of Earth observation (EO) data. To efficiently process and analyze the large-amount EO data, remote sensing has evolved into a multidisciplinary field, where machine learning and computer vision algorithms play an important role nowadays. At the start of this project course, students receive project topics as well as some information material in the field of computer vision for remote sensing. After setting the project teams and topics, a project environment is decided (with the suitable tools for a team work) with the assistance of the lecturer. Then, project planning, coordination and development start. During the weekly project meetings, each project team presents progress and then further steps are decided in consultation with the lecturer. The project is concluded with final reports as well as a final presentation. The general topics include but are not limited to: i) feature extraction and learning; ii) classification and retrieval of satellite images; iii) change detection and analysis of image time series; iv) object detection; v) multi-sensor and multi-source data fusion.

Module Components


All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Project Computer Vision for Remote SensingPJWiSe/SoSeEnglish6

Workload and Credit Points

Project Computer Vision for Remote Sensing (PJ):

Workload descriptionMultiplierHoursTotal
Pre/post processing15.012.0h180.0h
270.0h(~9 LP)
The Workload of the module sums up to 270.0 Hours. Therefore the module contains 9 Credits.

Description of Teaching and Learning Methods

This module contains a guided and self-organized project work. The students get a brief overview of the fundamentals and the recent developments in the area of computer vision for remote sensing. The students work in small teams on a chosen topic, and they present initial findings in an intermediate talk. Each team implements the project and presents insights, methods and results in a concluding talk. Finally, project reports are submitted.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Solid programming skills are required in at least one of the following programming languages: Java, C++, Python. Good knowledge in machine learning and computer vision is required.

Mandatory requirements for the module test application:

No information

Module completion



Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt



Test elements

(Deliverable assessment) Intermediate presentation10oralapprox. 15 minutes
(Deliverable assessment) Final presentation20oralapprox. 20 minutes
(Deliverable assessment) Technical Documentation10written5-10 pages
(Deliverable assessment) Scientific Report20written10-15 pages
(Deliverable assessment) Implementation40practicalapprox. 120 hours

Grading scale

Notenschlüssel »Notenschlüssel 1: Fak IV (1)«


Test description (Module completion)

The overall grade for the module consists of the results of the course work ('portfolio exam'). The following are included in the final grade: 1. Intermediate presentation (10p): The students present their initial findings and results on their topic. 2. Final presentation (20p): The students present their final findings/results. 3. Technical Documentation (10p): The students prepare a technical documentation of their codes. 4. Scientific Report (20p): The students summarize their final findings, methods and results in a written scientific report. 5. Implementation (40p): The students work in a team on a selected topic and develop its prototypical implementation.

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 9.

Registration Procedures

Students intending to take this project course need to follow the instructions on the RSiM website for pre-semester application. Within the first 3 weeks after the commencement of the project, students will have to register for the module at Moses - MTS (university examination protocol tool) and, additionally, at ISIS for teaching materials and communication.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable


Electronical lecture notes

Availability:  unavailable



Recommended literature
R. Szeliski, Computer Vision: Algorithms and Applications, Springer, 1st Edition, 2010.
T. M. Lillesand, R.W. Kiefer, J.W. Chipman, Remote Sensing and Image Interpretation, John Wiley & Sons Verlag, 2008

Assigned Degree Programs

This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computer Engineering (M. Sc.)110SoSe 2023WiSe 2023/24
Computer Science (Informatik) (M. Sc.)18SoSe 2023WiSe 2023/24
Elektrotechnik (M. Sc.)14SoSe 2023WiSe 2023/24


No information