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Overview of LifeCLEF 2020: A System-Oriented Evaluation of Automated Species Identification and Species Distribution Prediction

Alexis Joly 1 Hervé Goëau 2, 3 Stefan Kahl 4 Benjamin Deneu 1 Willem-Pier Vellinga 5 Maximilien Servajean 6 Elijah Cole 7 Lukáš Picek 8 Rafael Ruiz de Castañeda 9 Isabelle Bolon 9 Andrew Durso 9 Titouan Lorieul 1 Christophe Botella 10 Hervé Glotin 11 Julien Champ 1 Ivan Eggel 12 Pierre Bonnet 2, 3 Henning Müller 13
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 Cirad-BIOS - Département Systèmes Biologiques
4 Cornell University [New York]
5 Xeno-canto foundation
6 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
7 CALTECH - California Institute of Technology
8 University of West Bohemia [Plzeň ]
9 University of Geneva [Switzerland]
10 LECA - Laboratoire d'Ecologie Alpine
11 LIS - Laboratoire d'Informatique et Systèmes
12 HES-SO Valais-Wallis - Haute École Spécialisée de Suisse Occidentale Valais-Wallis
13 HES-SO - Haute Ecole Spécialisée de Suisse Occidentale
Abstract : 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
Keywords : LifeCLEF 2020 Automated Species Identification PlantCLEF BirdCLEF GeoLifeCLEF
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Submitted on : Monday, October 19, 2020 - 11:17:44 AM
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INRAE | LIS-LAB | ZENITH | LIRMM | MIPS | B3ESTE | AMAP | LECA | CIRAD | UNIV-SAVOIE | INRAEOCCITANIEMONTPELLIER | UNIV-TLN | CNRS | INRIA | UNIV-AMU | UNIV-MONTPELLIER | IRD | UGA | ADVANSE

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Alexis Joly, Hervé Goëau, Stefan Kahl, Benjamin Deneu, Willem-Pier Vellinga, et al.. Overview of LifeCLEF 2020: A System-Oriented Evaluation of Automated Species Identification and Species Distribution Prediction. CLEF 2020 - 11th International Conference of the Cross-Language Evaluation Forum for European Languages, Sep 2020, Thessaloniki, Greece. pp.342-363, ⟨10.1007/978-3-030-58219-7_23⟩. ⟨hal-02945382⟩

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