Overview of LifeCLEF 2020: A system-oriented evaluation of automated species identification and species distribution prediction

Joly Alexis, Goeau Hervé, Kahl Stefan, Deneu Benjamin, Servajean Maximilien, Cole Elijah, Picek Lukáš, Ruiz de Castañeda Rafael, Bolon Isabelle, Durso Andrew, Lorieul Titouan, Botella Christophe, Glotin Hervé, Champ Julien, Eggel Ivan, Vellinga Willem-Pier, Bonnet Pierre, Müller Henning. 2020. Overview of LifeCLEF 2020: A system-oriented evaluation of automated species identification and species distribution prediction. In : Experimental IR meets multilinguality, multimodality, and interaction: 11th International Conference of the CLEF Association, CLEF 2020, Thessaloniki, Greece, September 22–25, 2020, Proceedings. Arampatzis Avi (ed.), Kanoulas Evangelos (ed.), Tsikrika Theodora (ed.), Vrochidis Stefanos (ed.), et Al.. Cham : Springer, 342-363. (Lecture Notes in Computer Science, 12260) ISBN 978-3-030-58218-0 International Conference of the Cross-Language Evaluation Forum for European Languages, Thessaloniki, Grèce, 22 Septembre 2020/25 Septembre 2020.

https://doi.org/10.1007/978-3-030-58219-7_23

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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 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.

Mots-clés libres : Species identification, Deep Learning, Multimedia data, Evaluation, Automated system

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