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Overview of LifeCLEF 2020: A System-Oriented Evaluation of Automated Species Identification and Species Distribution Prediction
Conference proceeding   Open access   Peer reviewed

Overview of LifeCLEF 2020: A System-Oriented Evaluation of Automated Species Identification and Species Distribution Prediction

Alexis Joly, Hervé Goëau, Stefan Kahl, Benjamin Deneu, Willem-Pier Vellinga, Maximilien Servajean, Elijah Cole, Lukáš Picek, Rafael Ruiz de Castañeda, Isabelle Bolon, …
Experimental IR Meets Multilinguality, Multimodality, and Interaction11th International Conference of the CLEF Association, CLEF 2020, Thessaloniki, Greece, September 22–25, 2020, Proceedings, Vol.12260, pp.342-363
Lecture Notes in Computer Science
CLEF 2020 - 11th International Conference of the Cross-Language Evaluation Forum for European Languages
09-15-2020

Abstract

Biodiversity Biodiversity and Ecology Botanics Ecology, environment Environmental Sciences Systematics, Phylogenetics and taxonomy Vegetal Biology Ecosystems Life Sciences
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
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https://doi.org/10.1007/978-3-030-58219-7_23View
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