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Overview of LifeCLEF 2021: an evaluation of Machine-Learning based Species Identification and Species Distribution Prediction

Abstract : Building 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.
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https://hal.inria.fr/hal-03415990
Contributor : Alexis Joly Connect in order to contact the contributor
Submitted on : Friday, November 5, 2021 - 8:35:52 AM
Last modification on : Friday, November 18, 2022 - 9:26:43 AM
Long-term archiving on: : Sunday, February 6, 2022 - 6:19:26 PM

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Alexis Joly, Hervé Goëau, Stefan Kahl, Lukáš Picek, Titouan Lorieul, et al.. Overview of LifeCLEF 2021: an evaluation of Machine-Learning based Species Identification and Species Distribution Prediction. CLEF 2021 - 12th International Conference of the CLEF Association, Sep 2021, Virtual Event, France. pp.371-393, ⟨10.1007/978-3-030-85251-1_24⟩. ⟨hal-03415990⟩

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