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Overview of LifeCLEF 2021: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction

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Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12880))

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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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Notes

  1. 1.

    https://www.cbd.int/.

  2. 2.

    http://www.lifeclef.org/.

  3. 3.

    http://www.imageclef.org/.

  4. 4.

    https://www.aicrowd.com.

  5. 5.

    https://www.kaggle.com.

  6. 6.

    http://portal.idigbio.org/portal/search.

  7. 7.

    https://explore.recolnat.org/search/botanique/type=index.

  8. 8.

    https://eol.org/docs/what-is-eol/data-services.

  9. 9.

    https://www.xeno-canto.org/.

  10. 10.

    https://www.kaggle.com/c/birdclef-2021.

  11. 11.

    https://www.fsa.usda.gov.

  12. 12.

    https://geoservices.ign.fr.

  13. 13.

    http://osr-cesbio.ups-tlse.fr/~oso/posts/2017-03-30-carte-s2-2016/.

  14. 14.

    https://lpdaac.usgs.gov/products/srtmgl1v003/.

  15. 15.

    https://www.kaggle.com/c/geolifeclef-2021/.

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No\(^{\circ }\) 863463 (Cos4Cloud project), and the support of #DigitAG.

Author information

Authors and Affiliations

  1. Inria, LIRMM, Univ Montpellier, CNRS, Montpellier, France

    Alexis Joly, Titouan Lorieul & Benjamin Deneu

  2. CIRAD, UMR AMAP, Montpellier, Occitanie, France

    Hervé Goëau & Pierre Bonnet

  3. Univ. Toulon, Aix Marseille Univ., CNRS, LIS, DYNI Team, Marseille, France

    Hervé Glotin

  4. Xeno-canto Foundation, Amsterdam, The Netherlands

    Robert Planqué & Willem-Pier Vellinga

  5. HES-SO, Sierre, Switzerland

    Ivan Eggel & Henning Müller

  6. KLYCCB, Cornell Lab of Ornithology, Cornell University, Ithaca, USA

    Stefan Kahl & Holger Klinck

  7. LIRMM, AMI, Univ. Paul Valéry Montpellier, Univ. Montpellier, CNRS, Montpellier, France

    Maximillien Servajean

  8. ISG, Department of Community Health and Medicine, UNIGE, Geneva, Switzerland

    Isabelle Bolon & Rafael Ruiz de Castañeda

  9. Department of Computing and Mathematical Sciences, Caltech, Pasadena, USA

    Elijah Cole

  10. Department of Cybernetics, FAV, University of West Bohemia, Pilsen, Czechia

    Lukáš Picek

  11. Department of Biological Sciences, Florida Gulf Coast University, Fort Myers, USA

    Andrew Durso

  12. Google LLC, San Francisco, USA

    Tom Denton

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Correspondence to Alexis Joly .

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Editors and Affiliations

  1. Arizona State University, Tempe, AZ, USA

    K. Selçuk Candan

  2. Politehnica University of Bucharest, Bucharest, Romania

    Bogdan Ionescu

  3. Université Grenoble Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  4. Aalborg University Copenhagen, Copenhagen, Denmark

    Birger Larsen

  5. HES-SO Valais-Wallis, Sierre, Switzerland

    Henning Müller

  6. University of Montpellier, Montpellier, France

    Alexis Joly

  7. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  8. TU Wien, Vienna, Austria

    Florina Piroi

  9. University of Padua, Padova, Italy

    Guglielmo Faggioli

  10. University of Padua, Padova, Italy

    Nicola Ferro

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Joly, A. et al. (2021). Overview of LifeCLEF 2021: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction. In: Candan, K.S., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2021. Lecture Notes in Computer Science(), vol 12880. Springer, Cham. https://doi.org/10.1007/978-3-030-85251-1_24

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