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Deep Learning for the Analysis of Remote Sensing Imagery from Nano Satellites - Einzelansicht

Grunddaten
Veranstaltungsart Projektveranstaltung Langtext
Veranstaltungsnummer 144851 Kurztext
Semester SoSe 2021 SWS 4
Erwartete Teilnehmer/-innen 15 Studienjahr
Max. Teilnehmer/-innen 15
Credits 5 Belegung Belegpflicht
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Sprache englisch
Termine Gruppe: [unbenannt] iCalendar Export für Outlook
  Tag Zeit Rhythmus Dauer Raum Raum-
plan
Lehrperson Status Bemerkung fällt aus am Max. Teilnehmer/-innen
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Do. 16:00 bis 18:00 woch 15.04.2021 bis 22.07.2021           
Gruppe [unbenannt]:
 


Zugeordnete Personen
Zugeordnete Personen Zuständigkeit
Knoth, Christian, Dr. verantwort
Pebesma, Edzer, Prof. Dr. verantwort
Studiengänge
Abschluss - Studiengang Sem ECTS Bereich Teilgebiet
Bachelor - Geoinformatik (82 807 13) - 5
Bachelor - Geoinformatik (82 807 9) - 5
Master - Geoinformatics (88 E62 12) - 5
Master - Geoinformatics (88 E62 8) - 5
Master - Geoinformatics and Spatial Data Science (88 F26 21) - 5
Bachelor - Geoinformatik (82 807 21) - 5
Prüfungen / Module
Prüfungsnummer Modul
29003 Projekt - Bachelor Geoinformatik Version 2021
17002 Study Project in Geoinformatics - Master Geoinform and Spat. Data Version 2021
17001 Study Project in Geoinformatics - Master Geoinform and Spat. Data Version 2021
16004 Study Project Electives 2 - Master Geoinform and Spat. Data Version 2021
16003 Study Project Electives 1 - Master Geoinform and Spat. Data Version 2021
26003 Projekt - Bachelor Geoinformatik Version 2009
30002 Projekt - Bachelor Geoinformatik Version 2013
12002 Project in Interoperability - Master Geoinformatics Version 2008
13003 Project in Interoperability - Master Geoinformatics Version 2012
13003 Ausgewählte Probleme der Geoinformatik - Master Geoinformatics Version 2008
17002 Study Project Advanced Topics in Geographic Information Science - Master Geoinformatics Version 2012
18002 Project Computer Science - Master Geoinformatics Version 2012
Zuordnung zu Einrichtungen
Fachbereich 14 Geowissenschaften
Inhalt
Kommentar

The aim of this study project is to explore the suitability of convolutional neural networks (CNNs) for analyzing and classifying high-resolution satellite imagery from nano satellites. In addition, participants will get a basic understanding of the key steps for using deep learning in remote sensing image analysis, and how to apply them to a real-world example.

During the first part of the project, participants will get a practical introduction to deep learning in remote sensing using R. You will work on a tutorial that features the workflow with complete code examples (building of a CNN, data preparation, model training, prediction, …) and a minimal data set to get acquainted with the methodology and practical issues involved.

During the second part, you will work in groups on your own projects analyzing high resolution (3-4 m) satellite imagery in a practical use case. For the project work, each group will get access to a GPU-powered instance on amazon web services with the necessary software pre-installed.

Previous experience in deep learning will be useful but is not required. However, you will be required to show the engagement and curiosity necessary to work your way into the topic using the provided (and maybe additional) material.

Contact: christian.knoth@uni-muenster.de


Strukturbaum
Keine Einordnung ins Vorlesungsverzeichnis vorhanden. Veranstaltung ist aus dem Semester SoSe 2021 , Aktuelles Semester: WiSe 2022/23