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Machine Learning for Remote Sensing Data Analysis

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#40928 / #3

Seit WiSe 2023/24

English

Machine Learning for Remote Sensing Data Analysis

3

Demir, Begüm

Benotet

Portfolioprüfung

English

Zugehörigkeit


Fakultät IV

Institut für Technische Informatik und Mikroelektronik

Keine Angabe

Keine Angabe

Kontakt


EN 5

Fuchs, Martin Hermann Paul

m.fuchs@tu-berlin.de

http://rsim.berlin

Lernergebnisse

Participants of this seminar will acquire knowledge on advanced methodologies for the analysis of remote sensing images acquired by the last generation Earth observation satellite systems. In particular, this seminar course will provide students on the one hand an in-depth theoretical and practical knowledge on remote sensing image analysis; and on the other hand a know-how in one or more domains of applications, such as pixel-based and scene classification, learning-based image compression, image retrieval, visual question answering and image captioning. Moreover, the students will learn about the current developments in remote sensing and related data analysis methods, and how machine learning techniques can be employed to solve Earth observation questions.

Lehrinhalte

Remote sensing images are a rich information source for monitoring the Earth surface, e.g., for climate change analysis, urban area studies, forestry applications, risk and damage assessment, water quality assessment, crop monitoring. Due to the recent developments in both passive multispectral and hyperspectral sensors, and synthetic aperture radar active instruments, the role of this technology is becoming more and more important for understanding the dynamics of our planet. To efficiently process/analyze the data, remote sensing has evolved into a multidisciplinary field, where machine learning algorithms play an important role nowadays. In this seminar, students will review the current state of the art in the field of machine learning applied to remote sensing image analysis in the framework of different Earth observation applications. The general topics include but are not limited to: hashing methods, data augmentation, active learning, continual learning, federated learning, multimodal learning, compressed domain analysis, explanation methods and understanding memorization with applications to remote sensing image analysis.

Modulbestandteile

Keine Angabe

Die folgenden Veranstaltungen sind für das Modul obligatorisch:

LehrveranstaltungenArtNummerTurnusSpracheSWS ISIS VVZ
Machine Learning for Remote Sensing Data AnalysisSEMWiSeen2
ISIS-Kurssuche nach Veranstaltungen des Moduls

Arbeitsaufwand und Leistungspunkte

Machine Learning for Remote Sensing Data Analysis (SEM):

AufwandbeschreibungMultiplikatorStundenGesamt
Attendance15.02.0h30.0h
Pre/post processing15.04.0h60.0h
90.0h(~3 LP)
Der Aufwand des Moduls summiert sich zu 90.0 Stunden. Damit umfasst das Modul 3 Leistungspunkte.

Beschreibung der Lehr- und Lernformen

At the beginning of the semester there will be overview lectures that will provide some background concepts on remote sensing, different types of remote sensing images and the general remote sensing image processing/analysis chain. In addition, a set of primary literature works will be provided. Then, the students will further investigate the topics assigned to them in the seminar. A few weeks after they will give a short presentation of approx. 10-15 minutes (giving an introduction into the topic of their selected primary literature and summarizing the state of the art) that will be open to all seminar participants. In the further course of the semester the students will give a final presentation of approx. 20-25 minutes (presenting their selected paper and critically discussing it). In addition to the talk the students will also prepare a technical report with 5-8 pages (describing their topic, summarizing their selected paper and critically discussing it).

Voraussetzungen für die Teilnahme / Prüfung

Wünschenswerte Voraussetzungen für die Teilnahme an den Lehrveranstaltungen:

Good knowledge in Mathematics, especially linear algebra and statistics. Sound understanding of written and spoken English.

Verpflichtende Voraussetzungen für die Modulprüfungsanmeldung:

Dieses Modul hat keine Prüfungsvoraussetzungen.

Abschluss des Moduls

Benotung

Benotet

Prüfungsform

Portfolio examination

Art der Portfolioprüfung

100 Punkte insgesamt

Sprache(n)

English

Prüfungselemente

NamePunkteKategorieDauer/Umfang
(Deliverable assessment) Long Seminar Presentation40mündlich20-25 minutes
(Deliverable assessment) Short Seminar Presentation20mündlich10-15 minutes
(Deliverable assessment) Written Seminar Presentation40schriftlich5-8 pages

Notenschlüssel

Notenschlüssel »Notenschlüssel 2: Fak IV (2)«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt95.0pt90.0pt85.0pt80.0pt75.0pt70.0pt65.0pt60.0pt55.0pt50.0pt

Prüfungsbeschreibung (Abschluss des Moduls)

There are two oral portfolio deliverable assessments: a short seminar presentation (20 pts.), a long seminar presentation (40 pts.) There is one written portfolio deliverable assessment: a written seminar report (40 pts.)

Dauer des Moduls

Für Belegung und Abschluss des Moduls ist folgende Semesteranzahl veranschlagt:
1 Semester.

Dieses Modul kann in folgenden Semestern begonnen werden:
Wintersemester.

Maximale teilnehmende Personen

Die maximale Teilnehmerzahl beträgt 16.

Anmeldeformalitäten

Registration at Moses - MTS within the first 6 weeks of the semester and registration on ISIS for teaching materials and communication.

Literaturhinweise, Skripte

Skript in Papierform

Verfügbarkeit:  nicht verfügbar

 

Skript in elektronischer Form

Verfügbarkeit:  nicht verfügbar

 

Literatur

Empfohlene Literatur
G. Camps-Valls, and L. Bruzzone, Kernel methods for Remote Sensing Data Analysis, Wiley & Sons 2009
L. Bruzzone, B. Demir, A Review of Modern Approaches to Classification of Remote Sensing Data, in Land Use and Land Cover Mapping Europe, Practices and Trends, Eds: I. Manakos, M. Braun, EARSeL Book Series, Springer Verlag, Chapter 9, 2014, pp. 127-143.
E. Alpaydin, Introduction to Machine Learning, MIT Press, Cambridge, Massachusetts, London, England, 2004.

Zugeordnete Studiengänge


Diese Modulversion wird in folgenden Studiengängen verwendet:

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computer Engineering (M. Sc.)118WiSe 2023/24WiSe 2024/25
Computer Science (Informatik) (M. Sc.)115WiSe 2023/24WiSe 2024/25
Elektrotechnik (M. Sc.)19WiSe 2023/24WiSe 2024/25
ICT Innovation (M. Sc.)13WiSe 2023/24WiSe 2024/25
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)13WiSe 2023/24WiSe 2024/25
Wirtschaftsingenieurwesen (M. Sc.)16WiSe 2023/24WiSe 2024/25

Studierende anderer Studiengänge können dieses Modul ohne Kapazitätsprüfung belegen.

Sonstiges

Keine Angabe