Overview of LifeCLEF 2020 : a system-oriented evaluation of automated species identification and species distribution prediction

Joly, Alexis (Inria, LIRMM, Montpellier, France) ; Goëau, Hervé (CIRAD, UMR AMAP, France) ; Kahl, Stefan (INRAE, UMR AMAP, France) ; Deneu, Benjamin (Inria, LIRMM, Montpellier, France) ; Servajean, Maximillien (Universite Paul Valery, Montpellier, France) ; Cole, Elijah (Caltech, USA) ; Picek, Lukás (University of West Bohemia, Czechia) ; Ruiz de Castañeda, Rafael (University of Geneva, Switzerland) ; Bolon, Isabelle (University of Geneva, Switzerland) ; Durso, Andrew (Florida Gulf Coast University) ; Lorieul, Titouan (Inria, LIRMM, Montpellier, France) ; Botella, Christophe (CNRS, LECA, France) ; Glotin, Hervé (Universite de Toulon, CNRS, LIS, DYNI, Marseille, France) ; Champ, Julien (Inria, LIRMM, Montpellier, France) ; Eggel, Ivan (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Vellinga, Willem-Pier (Xeno-canto Foundation, The Netherlands) ; 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: snake identification based on image and geographic location.


Conference Type:
published full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Thessaloniki, Greece, 22-25 September 2020
Date:
2020-09
Thessaloniki, Greece
22-25 September 2020
Pagination:
Pp. 342-363
Published in:
Proceedings of International conference of the cross-language evaluation forum for European languages (CLEF 2020)
Series Statement:
Lecture notes in computer science, vol. 12260
DOI:
ISSN:
0302-9743
ISBN:
978-3-030-58218-0
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-11-19, last modified 2020-11-20

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