LifeCLEF 2019: Biodiversity Identification and Prediction Challenges

Alexis Joly 1 Hervé Goëau 2 Christophe Botella 2, 1 Stefan Kahl 3 Marion Poupard 4 Maximilien Servajean 5 Hervé Glotin 6, 4 Pierre Bonnet 2 Willem-Pier Vellinga 7 Robert Planqué 7 Jan Schlüter 4 Fabien-Robert Stöter 1 Henning Müller 8
1 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
2 UMR AMAP - Botanique et Modélisation de l'Architecture des Plantes et des Végétations
3 Chemnitz University of Technology
4 LSIS - Laboratoire des Sciences de l'Information et des Systèmes
5 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
6 UTLN - Université de Toulon
7 Xeno-canto foundation
8 HES-SO Valais-Wallis - Haute École Spécialisée de Suisse Occidentale Valais-Wallis
Abstract : 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.
Keywords : Species distribution model Bird identification Plant identification Species prediction Informatics Machine learning Species identification Biodiversity
Type de document :
Communication dans un congrès
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https://hal.umontpellier.fr/hal-02273257
Contributeur : Yannick Brohard <>
Soumis le : mercredi 28 août 2019 - 16:43:57
Dernière modification le : lundi 9 septembre 2019 - 10:25:23

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  • HAL Id : hal-02273257, version 1

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UNIV-MONTPELLIER | INRA | LSIS | ZENITH | LIRMM | AGROPOLIS | CIRAD | MIPS | INRIA | LIS-LAB | B3ESTE | AMAP | UNIV-TLN | UNIV-AMU | UNIV-HESAM | ADVANSE

Citation

Alexis Joly, Hervé Goëau, Christophe Botella, Stefan Kahl, Marion Poupard, et al.. LifeCLEF 2019: Biodiversity Identification and Prediction Challenges. 41st European Conference on IR Research, ECIR 2019, Apr 2019, Cologne, Germany. pp.275-282. ⟨hal-02273257⟩

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