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dc.contributor.authorJoly, Alexis
dc.contributor.authorGoëau, Hervé
dc.contributor.authorKahl, Stefan
dc.contributor.authorPicek, Lukáš
dc.contributor.authorLorieul, Titouan
dc.contributor.authorCole, Elijah
dc.contributor.authorDeneu, Benjamin
dc.contributor.authorServajean, Maximillien
dc.contributor.authorDurso, Andrew
dc.contributor.authorBolon, Isabelle
dc.contributor.authorGlotin, Hervé
dc.contributor.authorPlanqué, Robert
dc.contributor.authorde Castañeda, Rafael Ruiz
dc.contributor.authorVellinga, Willem-Pier
dc.contributor.authorKlinck, Holger
dc.contributor.authorDenton, Tom
dc.contributor.authorEggel, Ivan
dc.contributor.authorBonnet, Pierre
dc.contributor.authorMüller, Henning
dc.date.accessioned2022-03-28T10:00:30Z-
dc.date.available2022-03-28T10:00:30Z-
dc.date.issued2021
dc.identifier.citationJOLY, A. GOËAU, H. KAHL, S. PICEK, L. LORIEUL, T. COLE, E. DENEU, B. SERVAJEAN, M. DURSO, A. BOLON, I. GLOTIN, H. PLANQUÉ, R. DE CASTAÑEDA, RR. VELLINGA, W. KLINCK, H. DENTON, T. EGGEL, I. BONNET, P. MÜLLER, H. Overview of LifeCLEF 2021: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction. In Experimental IR Meets Multilinguality, Multimodality, and Interaction 12th International Conference of the CLEF Association, CLEF 2021, Virtual Event, September 21–24, 2021, Proceedings. Cham: Springer Nature, 2021. s. 371-393. ISBN: 978-3-030-85250-4 , ISSN: 0302-9743cs
dc.identifier.isbn978-3-030-85250-4
dc.identifier.issn0302-9743
dc.identifier.uri2-s2.0-85115882091
dc.identifier.urihttp://hdl.handle.net/11025/47275
dc.format23 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherSpringer Natureen
dc.relation.ispartofseriesExperimental IR Meets Multilinguality, Multimodality, and Interaction 12th International Conference of the CLEF Association, CLEF 2021, Virtual Event, September 21–24, 2021, Proceedingsen
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© Springeren
dc.titleOverview of LifeCLEF 2021: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Predictionen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.rights.accessrestrictedAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedBuilding accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants and animals is hindering the aggregation of new data and knowledge. Identifying and naming living plants or animals is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this gap is a key step towards enabling effective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2021 edition proposes four data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: cross-domain plant identification based on herbarium sheets, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: remote sensing based prediction of species, and (iv) SnakeCLEF: Automatic Snake Species Identification with Country-Level Focus.en
dc.subject.translatedBiodiversity conservationen
dc.subject.translatedBiodiversity monitoringen
dc.subject.translatedBird speciesen
dc.subject.translatedCross-domainen
dc.subject.translatedGeneral publicsen
dc.subject.translatedMachine-learningen
dc.subject.translatedMonitoring systemen
dc.subject.translatedPlant identificationen
dc.subject.translatedSpecies distributionsen
dc.subject.translatedSpecies identificationen
dc.subject.translatedForecastingen
dc.identifier.doi10.1007/978-3-030-85251-1_24
dc.type.statusPeer-revieweden
dc.identifier.obd43933883
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