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ФИНКИ

Subject

Application of Data Science to Earth Observation

1. Course Title Application of Data Science to Earth Observation
Applying data science to Earth observation data
2. Code m23_s_015
3. Study Programme —
4. Organizer of the study programme (unit, institute, department or division) Faculty of Computer Science and Engineering
5. Degree level (first, second, third cycle) Second cycle
6. Academic year / semester 10 / Summer
7. Number of ECTS credits 6
8. Teacher Ivan Kitanovski, Ivica Dimitrovski, Katarina Trojachancev Dineva
9. Prerequisites for enrolling in the course —
10. Objectives of the course programme (competences) Upon completion of the course, students are expected to acquire knowledge of processing, analyzing, and applying data science to Earth observations. In this context, the types of data obtained from Earth observations are defined. The content also includes methods for data collection and storage, as well as ways to automatically analyze them using machine learning techniques. Upon completion of the course, the student is expected to know and understand the challenges that arise in analyzing Earth observation data and to be able to apply methods for their analysis.
11. Course content Introduction and Overview - basic concepts; terminology; Earth observation data; applications and areas of interest. Satellite images – overview of different types of data sources, storage methods, and basic data preprocessing techniques. Drone, aircraft, and other types of data sources – overview of data types and challenges. Classification of Earth observation data - methods for automatic labeling of the content depicted and practical application, Semantic segmentation of satellite imagery - methods for automatically labeling specific parts of the images with their semantic meaning, Object Detection in Satellite Images - methods for locating and labeling objects of interest in Earth observation data, Detection of instances of different objects - review and development of methods for detecting and labeling individual objects, Temporal analysis of Earth observation data - a review of methods for analyzing time series data and their application for monitoring changes on Earth.
12. Learning methods Lectures, exercises, independent work, project assignments, seminar papers
13. Total available time 6 ECTS x 30 hours = 180 hours
14. Distribution of available time 45 + 15 + 30 + 50 + 40 = 180 hours
15. Forms of teaching activities
15.1. Lectures - theoretical instruction 45 hours
15.2. Exercises (laboratory, auditory), seminars, teamwork 15 hours
16. Other forms of activities
16.1. Project assignments 50 hours
16.2. Independent assignments 30 hours
16.3. Home study 40 hours
17. Assessment method
17.1. Tests 0 points
17.2. Seminar paper / 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 points5 (five) (F)
from 51 to 60 points6 (six) (E)
from 61 to 70 points7 (seven) (D)
from 71 to 80 points8 (eight) (C)
from 81 to 90 points9 (nine) (B)
from 91 to 100 points10 (ten) (A)
19. Requirement for obtaining a signature and taking the final exam NULL
20. Language of instruction Macedonian or English
21. Method for monitoring the quality of teaching internal evaluation and surveys
22. Literature
22.1. Required literature
1. Zhe Jiang, Shashi Shekhar | Spatial Big Data Science: Classification Techniques for Earth Observation Imagery 1st ed. 2017 Edition, Kindle Edition | Springer | 2017
2. Pierre-Philippe Mathieu, Christoph Aubrecht | Satellite Image Analysis: Clustering and Classification | Springer | 2018
3. Pierre-Philippe Mathieu | Earth Observation Open Science and Innovation | Springer | 2018
4.
22.2. Additional literature
No. Author Title Publisher Year
Деканат Dean’s Office Студии Studies Акти Acts Проекти Projects