elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Impressum | Datenschutz | Kontakt | English
Schriftgröße: [-] Text [+]

AI4EO: Learning from Human Uncertainty

Koller, Christoph und Kauermann, Göran und Zhu, Xiao Xiang (2022) AI4EO: Learning from Human Uncertainty. LPS 2022, 2022-05-23 - 2022-05-27, Bonn, Germany.

Dieses Archiv kann nicht den Volltext zur Verfügung stellen.

Kurzfassung

As many other research fields, remote sensing has been greatly impacted by machine and deep learning and benefits from technological and computational advances. In the recent years, a considerable effort has been spent on deriving not just accurate, but also reliable modeling techniques. In the particular framework of image classification, this reliability is validated by e.g. checking whether the confidence in the model prediction adequately describes the true certainty of the model when confronted with unseen data. We investigate this reliability in the framework of classifying satellite images into different land cover classes. More precisely, we use the So2Sat LCZ42 data set comprised of Sentinel-1 and Sentinel-2 image pairs. Those were classified into 17 categories by a team of two labelers, following the Local Climate Zone (LCZ) classification scheme. As a novelty, we make explicit use of the so-termed evaluation set which was additionally produced by the authors of the LCZ42 data set. In this supplementary study, a subset of the initial data was re-labeled by 10 different remote sensing experts, which independently of one another re-cast their label votes for each satellite image. The resulting sets of label votes contain a notion of human uncertainty associated with the underlying satellite images. In the following, we try to explicitly incorporate this uncertainty into the training process of a neural network classifier and investigate its impact on model performance. Also, the earlier introduced definition of reliability is checked and compared to a more common modeling approach. The more common approach is using a single ground truth as label, which is derived from the majority vote of the individual expert label votes.

elib-URL des Eintrags:https://elib.dlr.de/186675/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:AI4EO: Learning from Human Uncertainty
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Koller, ChristophChristoph.Koller (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Kauermann, Görangoeran.kauermann (at) lmu.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Zhu, Xiao Xiangxiao.zhu (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:Mai 2022
Referierte Publikation:Nein
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:Local Climate Zone Classification, Calibration, Uncertainty, Remote Sensing
Veranstaltungstitel:LPS 2022
Veranstaltungsort:Bonn, Germany
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:23 Mai 2022
Veranstaltungsende:27 Mai 2022
Veranstalter :ESA
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Künstliche Intelligenz
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > EO Data Science
Hinterlegt von: Koller, Christoph
Hinterlegt am:14 Jun 2022 09:28
Letzte Änderung:24 Apr 2024 20:48

Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags

Blättern
Suchen
Hilfe & Kontakt
Informationen
electronic library verwendet EPrints 3.3.12
Gestaltung Webseite und Datenbank: Copyright © Deutsches Zentrum für Luft- und Raumfahrt (DLR). Alle Rechte vorbehalten.