LifeCLEF 2019: Biodiversity identification and prediction challenges

Joly Alexis, Goeau Hervé, Botella Christophe, Kahl Stefan, Poupard Pascal, Servajean Maximilien, Glotin Hervé, Bonnet Pierre, Vellinga Willem-Pier, Planqué Robert, Schlüter Jan, Stöter Fabian-Robert, Müller Henning. 2019. LifeCLEF 2019: Biodiversity identification and prediction challenges. In : Advances in information retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14–18, 2019, Proceedings, Part II. Azzopardi Leif (ed.), Stein Benno (ed.), Fuhr Nobert (ed.), Mayr Philipp (ed.), Hauff Claudia (ed.), Hiemstra Djoerd (ed.). Cham : Springer, 275-282. (Lecture Notes in Computer Science, 11438, 11438) ISBN 978-3-030-15718-0 European Conference on IR Research (ECIR 2019). 41, Cologne, Allemagne, 14 Septembre 2019/18 Septembre 2019.

https://doi.org/10.1007/978-3-030-15719-7_37

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Résumé : Building accurate knowledge of the identity, the geographic distribution and the evolution of living species is essential for a sustainable development of humanity, as well as for biodiversity conservation. However, the burden of the routine identification of plants and animals in the field is strongly penalizing the aggregation of new data and knowledge. Identifying and naming living plants or animals is actually almost impossible for the general public and often a difficult task for professionals and naturalists. Bridging this gap is a key challenge towards enabling effective biodiversity information retrieval systems. The LifeCLEF evaluation campaign, presented in this paper, aims at boosting and evaluating the advances in this domain since 2011. In particular, the 2019 edition proposes three data-oriented challenges related to the identification and prediction of biodiversity: (i) an image-based plant identification challenge, (ii) a bird sounds identification challenge and (iii) a location-based species prediction challenge based on spatial occurrence data and environmental tensors.

Mots-clés libres : Biodiversity, Biodiversity informatics, Machine learning, Species identification, Species recommendation

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