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A deep learning movement prediction framework for identifying anomalies in animal-environment interactions

Schwalb-Willmann, Jakob (2018) A deep learning movement prediction framework for identifying anomalies in animal-environment interactions. Master's, Julius-Maximilians-Universität Würzburg.

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Item URL in elib:https://elib.dlr.de/123853/
Document Type:Thesis (Master's)
Title:A deep learning movement prediction framework for identifying anomalies in animal-environment interactions
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Schwalb-Willmann, JakobUNSPECIFIEDUNSPECIFIED
Date:23 October 2018
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:55
Status:Published
Keywords:movement prediction, framework, anomalis in animal-enviroment
Institution:Julius-Maximilians-Universität Würzburg
Department:Lehrstuhl für Fernerkundung
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Geowissenschaftl. Fernerkundungs- und GIS-Verfahren
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center
Deposited By: Wöhrl, Monika
Deposited On:29 Nov 2018 09:36
Last Modified:13 Dec 2018 12:16

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