LifeCLEF 2021 Teaser: Biodiversity identification and prediction challenges

Joly Alexis, Goeau Hervé, Kahl Stefan, Glotin Hervé, Cole Elijah, Deneu Benjamin, Servajean Maximilien, Lorieul Titouan, Vellinga Willem-Pier, Bonnet Pierre, Eggel Ivan, Müller Henning. 2021. LifeCLEF 2021 Teaser: Biodiversity identification and prediction challenges. In : Advances in Information Retrieval: 43rd European conference on IR research, ECIR 2021, virtual event, March 28-April 1, 2021, Proceedings, Part II. Hiemstra Djoerd (ed.), Moens Marie-Francine (ed.), Mothe Josiane (ed.), Perego Raffaele (ed.), Potthast Martin (ed.), Sebastiani Fabrizio (ed.). Cham : Springer, 601-607. (Lecture Notes in Computer Science, 12657, 12656) LifeCLEF 2021, s.l., Suisse, 28 Mars 2021/1 Avril 2021.

Note générale : Le congrès s'est tenu en ligne

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 2021 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, IA, Species identification, Species prediction, Plant identification, Bird identification, Species distribution model

Agences de financement européennes : European Commission

Agences de financement hors UE : Agence Nationale de la Recherche

Programme de financement européen : H2020

Projets sur financement : (FRA) Statistical Modeling and Inference for unsupervised Learning at largE-Scale, (EU) Co-designed Citizen Observatories Services for the EOS-Cloud

Auteurs et affiliations

• Joly Alexis, INRIA (FRA)
• Goeau Hervé, CIRAD-BIOS-UMR AMAP (FRA)
• Kahl Stefan, Chemnitz University of Technology (DEU)
• Glotin Hervé, Université de Toulon et du Var (FRA)
• Cole Elijah, Caltech (USA)
• Deneu Benjamin, CIRAD-BIOS-UMR AMAP (FRA)
• Servajean Maximilien, CNRS (FRA)
• Lorieul Titouan, INRIA (FRA)
• Vellinga Willem-Pier, Xeno-canto foundation (NLD)
• Bonnet Pierre, CIRAD-BIOS-UMR AMAP (FRA) ORCID: 0000-0002-2828-4389
• Eggel Ivan, HES (CHE)
• Müller Henning, HES (CHE)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/612796/)

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