Overview of LifeCLEF 2021: An evaluation of machine-learning based species identification and species distribution prediction

Joly Alexis, Goeau Hervé, Kahl Stefan, Picek Lukáš, Lorieul Titouan, Cole Elijah, Deneu Benjamin, Servajean Maximilien, Durso Andrew, Bolon Isabelle, Glotin Hervé, Planqué Robert, Ruiz de Castañeda Rafael, Vellinga Willem-Pier, Klinck Holger, Denton Tom, Eggel Ivan, Bonnet Pierre, Müller Henning. 2021. 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. Selçuk Candan K. (ed.), Ionescu Bogdan (ed.), Goeuriot Lorraine (ed.), Larsen Birger (ed.), Müller Henning (ed.), Joly Alexis (ed.), Maistro Maria (ed.), Piroi Florina (ed.), Faggioli Guglielmo (ed.), Ferro Nicola (ed.). Cham : Springer, 371-393. (Lecture Notes in Computer Science, 12880, 12880) ISBN 978-3-030-85251-1 International Conference of the Cross-Language Evaluation Forum for European Languages. 12, 21 Septembre 2021/24 Septembre 2021.

https://doi.org/10.1007/978-3-030-85251-1_24

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Résumé : 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 ezective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2021 edition pro- poses four data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: cross-domain plant identi}cation 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.

Agences de financement européennes : European Commission

Agences de financement hors UE : Agence Nationale de la Recherche

Programme de financement européen : H2020

Projets sur financement : (EU) Co-designed Citizen Observatories Services for the EOS-Cloud, (FRA) Institut Convergences en Agriculture Numérique

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