LifeCLEF 2020 teaser: Biodiversity identification and prediction challenges

Joly Alexis, Goeau Hervé, Kahl Stefan, Botella Christophe, Ruiz de Castañeda Rafael, Glotin Hervé, Cole Elijah, Champ Julien, Deneu Benjamin, Servajean Maximilien, Lorieul Titouan, Vellinga Willem-Pier, Stöter Fabian-Robert, Durso Andrew, Bonnet Pierre, Müller Henning. 2020. LifeCLEF 2020 teaser: Biodiversity identification and prediction challenges. In : Advances in information retrieval: 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14–17, 2020, Proceedings, Part II. Jose Joemon M. (ed.), Yilmaz Emine (ed.), Magalhães João (ed.), Castells Pablo (ed.), Ferro Nicola (ed.), Silva Mário J. (ed.), Martins Flávio (ed.). Cham : Springer, 542-549. (Lecture Notes in Computer Science, 12036, 12036) ISBN 978-3-030-45441-8 European Conference on IR Research (ECIR 2020). 42, Lisbonne, Portugal, 14 Avril 2020/17 Avril 2020.

https://doi.org/10.1007/978-3-030-45442-5_70

Communication avec actes

Version publiée - Anglais
Accès réservé aux personnels Cirad
Utilisation soumise à autorisation de l'auteur ou du Cirad.
ID598373.pdf
Télécharger (203kB) | Demander une copie

Prévisualisation

Version post-print - Anglais
Sous licence

.
LifeCLEF_ECIR2020.pdf
Télécharger (187kB) | Prévisualisation

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 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: image-based snake identification.

Mots-clés libres : Biodiversity, Machine learning, Artificial intelligence, Species identification, Species prediction

Auteurs et affiliations