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Overview of LifeCLEF 2020: A System-Oriented Evaluation of Automated Species Identification and Species Distribution Prediction

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Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12260))

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 in the field 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 2020 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: location-based prediction of species based on environmental and occurrence data, and (iv) SnakeCLEF: snake identification based on image and geographic location.

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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.

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

  6. 6.

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

  7. 7.

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

  8. 8.

    National Agriculture Image Program, https://www.fsa.usda.gov.

  9. 9.

    https://geoservices.ign.fr.

  10. 10.

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

  11. 11.

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

  12. 12.

    Most of the Stanford team’s methods were based on deep neural networks, but the authors informed us that they encounter convergence issues resulting in performance poorer than expected.

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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, Montpellier, France

    Alexis Joly, Benjamin Deneu, Titouan Lorieul & Julien Champ

  2. CIRAD, UMR AMAP, 34398, Montpellier, France

    Hervé Goëau & Pierre Bonnet

  3. AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France

    Hervé Goëau & Pierre Bonnet

  4. Aix Marseille Univ, Université de Toulon, CNRS, LIS, DYNI, Marseille, France

    Hervé Glotin

  5. Xeno-canto Foundation, The Hague, The Netherlands

    Willem-Pier Vellinga

  6. HES-SO, Sierre, Switzerland

    Ivan Eggel & Henning Müller

  7. Cornell Lab of Ornithology, Cornell University, Ithaca, USA

    Stefan Kahl

  8. LIRMM, Université Paul Valéry, University of Montpellier, CNRS, Montpellier, France

    Maximillien Servajean

  9. Institute of Global Health, Department of Community Health and Medicine, Faculty of Medicine, University of Geneva, Geneva, Switzerland

    Rafael Ruiz de Castañeda & Isabelle Bolon

  10. Caltech, Pasadena, USA

    Elijah Cole

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

    Lukáš Picek

  12. CNRS, LECA, Grenoble, France

    Christophe Botella

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

    Andrew Durso

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  1. Alexis Joly
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Correspondence to Alexis Joly .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece

    Avi Arampatzis

  2. University of Amsterdam, Amsterdam, The Netherlands

    Evangelos Kanoulas

  3. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Theodora Tsikrika

  4. Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece

    Stefanos Vrochidis

  5. Faculty of Library, Information and Media Science, University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Department of Computer Science, University of Copenhagen, Copenhagen, Denmark

    Christina Lioma

  7. Brown University, Providence, RI, USA

    Carsten Eickhoff

  8. LIMSI-CNRS, Orsay, France

    Aurélie Névéol

  9. Department of Information Engineering, University of Padova, Padua, Italy

    Linda Cappellato

  10. Department of Information Engineering, University of Padova, Padua, Italy

    Nicola Ferro

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Joly, A. et al. (2020). Overview of LifeCLEF 2020: A System-Oriented Evaluation of Automated Species Identification and Species Distribution Prediction. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2020. Lecture Notes in Computer Science(), vol 12260. Springer, Cham. https://doi.org/10.1007/978-3-030-58219-7_23

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