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WeightNameValue
1000 Titel
  • Mapping the extent of giant Antarctic icebergs with deep learning
1000 Autor/in
  1. Braakmann-Folgmann, Anne |
  2. Shepherd, Andrew |
  3. Hogg, David |
  4. Redmond, Ella |
1000 Verlag
  • Copernicus Publications
1000 Erscheinungsjahr 2023
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2023-11-09
1000 Erschienen in
1000 Quellenangabe
  • 17(11):4675-4690
1000 Copyrightjahr
  • 2023
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.5194/tc-17-4675-2023 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:p>Abstract. Icebergs release cold, fresh meltwater and terrigenous nutrients as they drift and melt, influencing the local ocean properties, encouraging sea ice formation and biological production. To locate and quantify the fresh water flux from Antarctic icebergs, changes in their area and thickness have to be monitored along their trajectories. While the locations of large icebergs are operationally tracked by manual inspection, delineation of their extent is not. Here, we propose a U-net approach to automatically map the extent of giant icebergs in Sentinel-1 imagery. This greatly improves the efficiency compared to manual delineations, reducing the time for each outline from several minutes to less than 0.01 s. We evaluate the performance of our U-net and two state-of-the-art segmentation algorithms (Otsu and k-means) on 191 images. For icebergs larger than those covered by the training data, we find that U-net tends to miss parts. Otherwise, U-net is more robust in scenes with complex backgrounds – ignoring sea ice, smaller regions of nearby coast or other icebergs – and outperforms the other two techniques by achieving an F1 score of 0.84 and an absolute median deviation in iceberg area of 4.1 %. </jats:p>
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/QnJhYWttYW5uLUZvbGdtYW5uLCBBbm5l|https://frl.publisso.de/adhoc/uri/U2hlcGhlcmQsIEFuZHJldw==|https://frl.publisso.de/adhoc/uri/SG9nZywgRGF2aWQ=|https://frl.publisso.de/adhoc/uri/UmVkbW9uZCwgRWxsYQ==
1000 Hinweis
  • DeepGreen-ID: f1893046f4d34e7fad158a69894a88a1 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
1000 Label
1000 Förderer
  1. Natural Environment Research Council |
1000 Fördernummer
  1. -
1000 Förderprogramm
  1. -
1000 Dateien
  1. Mapping the extent of giant Antarctic icebergs with deep learning
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Natural Environment Research Council |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6481755.rdf
1000 Erstellt am 2024-05-23T23:37:41.051+0200
1000 Erstellt von 322
1000 beschreibt frl:6481755
1000 Zuletzt bearbeitet Mon May 27 08:41:12 CEST 2024
1000 Objekt bearb. Mon May 27 08:41:12 CEST 2024
1000 Vgl. frl:6481755
1000 Oai Id
  1. oai:frl.publisso.de:frl:6481755 |
1000 Sichtbarkeit Metadaten public
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