LifeCLEF 2020 teaser : biodiversity identification and prediction challenges

Joly, Alexis (Inria, LIRMM, Montpellier, France) ; Goëau, Hervé (CIRAD, UMR AMAP, France) ; Kahl, Stefan (Chemnitz University of Technology, Germany) ; Botella, Christophe (Inria, LIRMM, Montpellier, France ; INRA, UMR AMAP, France) ; Ruiz De Castaneda, Rafael (University of Geneva, Switzerland) ; Glotin, Hervé (Aix Marseille Univ, Universite de Toulon, CNRS, LIS, DYNI, Marseille, France) ; Cole, Elijah (Caltech, US) ; Champ, Julien (Inria, LIRMM, Montpellier, France) ; Deneu, Benjamin (Inria, LIRMM, Montpellier, France) ; Servajean, Maximillien (LIRMM, Universite Paul Valery, University of Montpellier, CNRS, France) ; Lorieul, Titouan (Inria, LIRMM, Montpellier, France) ; Vellinga, Willem-Pier (Xeno-canto Foundation, The Netherlands) ; Stöter, Fabian-Robert (Inria, LIRMM, Montpellier, France) ; Durso, Andrew (University of Geneva, Switzerland) ; Bonnet, Pierre (CIRAD, UMR AMAP, France) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

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: image-based snake identification.


Note: Due to the COVID-19 outbreak, the 42nd European Conference on Information Retrieval (ECIR 2020) venue in Lisbon was cancelled. The proceedings of the online conference are however published according to the original schedule.


Keywords:
Conference Type:
published full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Lisbon, Portugal, 14-17 April 2020
Date:
2020-04
Lisbon, Portugal
14-17 April 2020
Pagination:
Pp. 542-549
Published in:
Proceedings of the 42nd European Conference on Information Retrieval (ECIR 2020)
Series Statement:
Lecture notes in computer science, vol. 12036
DOI:
ISSN:
0302-9743
ISBN:
978-3-030-45441-8
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-11-18, last modified 2020-11-20

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