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

• Published in: Experimental IR Meets Multilinguality, Multimodality, and Interaction Vol. 13390; pp. 257 - 285
• Main Authors: Joly, Alexis, Goëau, Hervé, Kahl, Stefan, Picek, Lukáš, Lorieul, Titouan, Cole, Elijah, Deneu, Benjamin, Servajean, Maximilien, Durso, Andrew, Glotin, Hervé, Planqué, Robert, Vellinga, Willem-Pier, Navine, Amanda, Klinck, Holger, Denton, Tom, Eggel, Ivan, Bonnet, Pierre, Šulc, Milan, Hrúz, Marek
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Switzerland Springer International Publishing AG 2022; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Biodiversity; Biodiversity and Ecology; Botanics; Ecology, environment; Ecosystems; Environmental Sciences; Life Sciences; Systematics, Phylogenetics and taxonomy; Vegetal Biology; LifeCLEF 2022; Species Distribution Prediction; Machine learning
• ISBN: 303113642X; 9783031136429; 3031136438; 9783031136436
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-13643-6_19

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 on a global scale, and (v) FungiCLEF: fungi recognition as an open set classification problem. This paper overviews the motivation, methodology and main outcomes of that five challenges.