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

Alexis Joly (1) , Hervé Goëau (2, 3) , Stefan Kahl (4) , Lukáš Picek (5) , Titouan Lorieul (1) , Elijah Cole (6) , Benjamin Deneu (1) , Maximilien Servajean (7, 8) , Andrew Durso (9) , Isabelle Bolon (10) , Hervé Glotin (11) , Robert Planqué (12) , Willem-Pier Vellinga (12) , Holger Klinck (4) , Tom Denton (13) , Ivan Eggel (14) , Pierre Bonnet (2, 3) , Henning Müller (14) , Milan Šulc (15)

1 ZENITH - Scientific Data Management
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 University of West Bohemia [Plzeň ]
6 CS CALTECH - Computer Science Department
7 LIRMM | ADVANSE - ADVanced Analytics for data SciencE
8 UPVM - Université Paul-Valéry - Montpellier 3
9 Florida Gulf Coast University
10 UNIGE - Université de Genève = University of Geneva
11 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)
12 Xeno-canto foundation
13 Google LLC
14 HES-SO - Haute Ecole Spécialisée de Suisse Occidentale
15 CTU - Czech Technical University in Prague

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.

Mots clés: Machine-Learning; Species Identification; LifeCLEF

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]; Biodiversité et Ecologie; Systématique, phylogénie et taxonomie; Botanique; Ecosystèmes

Alexis Joly, Hervé Goëau, Stefan Kahl, Lukáš Picek, Titouan Lorieul, et al.. LifeCLEF 2022 Teaser: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction. ECIR 2022 - 44th European Conference on Information Retrieval Research, Apr 2022, Stavanger, Norway. pp.390-399, ⟨10.1007/978-3-030-99739-7_49⟩. ⟨hal-03641389⟩