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Overview of LifeCLEF 2022: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction

  • Conference paper
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Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13390))

  • 1772 Accesses

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, animals and fungi is hindering the aggregation of new data and knowledge. Identifying and naming living organisms 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 2022 edition proposes five data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: very large-scale plant identification, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: remote sensing based prediction of species, (iv) SnakeCLEF: snake species identification on a global scale, and (v) FungiCLEF: fungi recognition as an open set classification problem. This paper overviews the motivation, methodology and main outcomes of that five challenges.

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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://www.fgvc.org/.

  7. 7.

    https://cvpr2022.thecvf.com/.

  8. 8.

    http://ceur-ws.org/.

  9. 9.

    https://www.aicrowd.com/challenges/lifeclef-2022-plant.

  10. 10.

    https://gbif.org/.

  11. 11.

    https://eol.org/.

  12. 12.

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

  13. 13.

    https://www.kaggle.com/c/birdclef-2022.

  14. 14.

    https://www.kaggle.com/c/geolifeclef-2022-lifeclef-2022-fgvc9/data.

  15. 15.

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

  16. 16.

    https://geoservices.ign.fr.

  17. 17.

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

  18. 18.

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

  19. 19.

    https://www.kaggle.com/c/geolifeclef-2022-lifeclef-2022-fgvc9.

  20. 20.

    https://www.kaggle.com/competitions/fungiclef2022.

  21. 21.

    https://www.inaturalist.com/.

  22. 22.

    https://www.kaggle.com/competitions/fungiclef2022.

  23. 23.

    https://apps.apple.com/us/app/atlas-of-danish-fungi/id1467728588.

  24. 24.

    https://play.google.com/store/apps/details?id=com.noque.svampeatlas.

  25. 25.

    https://svampe.databasen.org/.

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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

  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

    Maximilien Servajean

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

    Alexis Joly, Hervé Goëau, Stefan Kahl, Lukáš Picek, Titouan Lorieul, Elijah Cole, Benjamin Deneu, Maximilien Servajean, Andrew Durso, Hervé Glotin, Robert Planqué, Willem-Pier Vellinga, Amanda Navine, Holger Klinck, Tom Denton, Ivan Eggel, Pierre Bonnet, Milan Šulc & Marek Hrúz

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

    Lukáš Picek, Elijah Cole & Marek Hrúz

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

    Andrew Durso

  11. Google LLC, San Francisco, USA

    Tom Denton

  12. Rossum.ai, Prague, Czech Republic

    Milan Šulc

  13. Listening Observatory for Hawaiian Ecosystems, Univ. of Hawai’i at Hilo, Hilo, USA

    Amanda Navine

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Corresponding author

Correspondence to Alexis Joly .

Editor information

Editors and Affiliations

  1. University of Bologna, Forlì, Italy

    Alberto Barrón-Cedeño

  2. University of Padua, Padova, Italy

    Giovanni Da San Martino

  3. University of Bologna, Bologna, Italy

    Mirko Degli Esposti

  4. Instituto di Scienza e Tecnologie dell' Informazione “Alessandro Faedo”, Pisa, Italy

    Fabrizio Sebastiani

  5. University of Glasgow, Glasgow, UK

    Craig Macdonald

  6. University Milano-Bicocca, Milan, Italy

    Gabriella Pasi

  7. TU Wien, Vienna, Austria

    Allan Hanbury

  8. Leipzig University, Leipzig, Germany

    Martin Potthast

  9. University of Padua, Padova, Italy

    Guglielmo Faggioli

  10. University of Padua, Padova, Italy

    Nicola Ferro

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Joly, A. et al. (2022). Overview of LifeCLEF 2022: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction. In: Barrón-Cedeño, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2022. Lecture Notes in Computer Science, vol 13390. Springer, Cham. https://doi.org/10.1007/978-3-031-13643-6_19

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  • DOI: https://doi.org/10.1007/978-3-031-13643-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13642-9

  • Online ISBN: 978-3-031-13643-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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