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LifeCLEF 2022 Teaser: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction

Joly, Alexis and Goëau, Hervé and Kahl, Stefan and Picek, Lukáš and Lorieul, Titouan and Cole, Elijah and Deneu, Benjamin and Servajean, Maximilien and Durso, Andrew and Bolon, Isabelle and Glotin, Hervé and Planqué, Robert and Vellinga, Willem-Pier and Klinck, Holger and Denton, Tom and Eggel, Ivan and Bonnet, Pierre and Müller, Henning and Šulc, Milan (2022) LifeCLEF 2022 Teaser: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction. In: Advances in Information Retrieval. Lecture Notes in Computer Science. No.13186. Springer , Cham, pp. 390-399. ISBN 978-3-030-99738-0. https://resolver.caltech.edu/CaltechAUTHORS:20220713-681149500

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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, animals and fungi is hindering the aggregation of new data and knowledge. Identifying and naming living organisms 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 2022 edition proposes five data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: very large-scale plant identification, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: remote sensing based prediction of species, (iv) SnakeCLEF: Snake Species Identification in Medically Important scenarios, and (v) FungiCLEF: Fungi recognition from images and metadata.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/978-3-030-99739-7_49DOIArticle
https://rdcu.be/cRzz6PublisherFree ReadCube access
ORCID:
AuthorORCID
Joly, Alexis0000-0002-2161-9940
Goëau, Hervé0000-0003-3296-3795
Kahl, Stefan0000-0002-2411-8877
Picek, Lukáš0000-0002-6041-9722
Lorieul, Titouan0000-0001-5228-9238
Cole, Elijah0000-0001-6623-0966
Deneu, Benjamin0000-0003-0640-5706
Servajean, Maximilien0000-0002-9426-2583
Durso, Andrew0000-0002-3008-7763
Bolon, Isabelle0000-0001-5940-2731
Glotin, Hervé0000-0001-7338-8518
Planqué, Robert0000-0002-0489-5425
Vellinga, Willem-Pier0000-0003-3886-5088
Bonnet, Pierre0000-0002-2828-4389
Müller, Henning0000-0001-6800-9878
Šulc, Milan0000-0002-6321-0131
Additional Information:© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG. First Online: 05 April 2022. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No◦ 863463 (Cos4Cloud project), and the support of #DigitAG.
Funders:
Funding AgencyGrant Number
European Research Council (ERC)863463
Series Name:Lecture Notes in Computer Science
Issue or Number:13186
DOI:10.1007/978-3-030-99739-7_49
Record Number:CaltechAUTHORS:20220713-681149500
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220713-681149500
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115528
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:14 Jul 2022 15:51
Last Modified:14 Jul 2022 15:51

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