Applying data science for Earth observation data

1.

Subject title

Applying data science for Earth observation data

Примена на наука за податоците за набљудување на Земјата

2.

Code

m23_s_015

3.

Study program

Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Eco-informatics, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Statistics and Data Analytics, Software for embedded systems, IT management, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Еducation with ICT, Cloud Computing, Data science in computer science and engineering, Software Engineering, Software Engineering,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

ворн. проф. д-р Иван Китановски проф. д-р Ивица Димитровски ворн. проф. д-р Катарина Тројачанец Динева

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

After completing the course, students are expected to acquire knowledge of processing, analysis and application of science for country observations. In this context, the types of data obtained from country observations are defined. The content also includes the methods of collecting and storing data, as ways to automatically analyze them by means of machine learning methods. Upon completion of the course, the student is expected to know and understand the challenges that analyze the data of observations of the country and know how to apply methods for their analysis.

11.

Subject content:

Introduction and Review - Basic Concepts; terminology; Earth observations data; application and areas of interest. Satellite images - an overview of different types of data sources, ways of storage and basic methods for data coverage. Data from drones, aircraft and other types of sources - an overview of types of data and challenges. Country Observations Classification - Methods for automatic content marking and practical application, satellite satellite segmentation - methods for automatic marking specific parts of images with their semantic meaning, detection of objects in satellite images - location and location methods and marking of Objects of interest in country observations, detection of installations from different facilities - review and development of methods for detection and marking of individual objects, time analysis of country observations - Overview of Time Data Analysis Methods and their application to monitor earth changes.

12.

Learning methods:

Предавања, вежби, самостојна работа, проектни задачи, семинарски работи

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

45 + 15 + 30 + 50 + 40 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

45 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

15 hours

16.

Other forms of activities

16.1.

Project tasks

50 hours

16.2.

Independent tasks

30 hours

16.3.

Homework

40 hours

17.

Grading method

17.1.

Tests

0 points

17.2.

Seminar work / project (presentation: written and oral)

50 points

17.3.

Activities and learning

0 points

17.4.

Final exam

0 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

NULL

20.

Language of instruction

македонски или англиски

21.

Quality assurance method

интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature