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Overview of LifeCLEF 2021: an evaluation of Machine-Learning based Species Identification and Species Distribution Prediction
Conference proceeding   Open access   Peer reviewed

Overview of LifeCLEF 2021: an evaluation of Machine-Learning based Species Identification and Species Distribution Prediction

Alexis Joly, Hervé Goëau, Stefan Kahl, Lukáš Picek, Titouan Lorieul, Elijah Cole, Benjamin Deneu, Maximilien Servajean, Andrew Durso, Isabelle Bolon, …
Experimental IR Meets Multilinguality, Multimodality, and Interaction 12th International Conference of the CLEF Association, CLEF 2021, Virtual Event, September 21-24, 2021, Proceedings, Vol.12880, pp.371-393
Lecture Notes in Computer Science
CLEF 2021 - 12th International Conference of the Cross-Language Evaluation Forum for European Languages
09-14-2021

Abstract

Artificial Intelligence Biodiversity and Ecology Computer Vision and Pattern Recognition Environment and Society Environmental Sciences Modeling and Simulation Multimedia Computer Science Machine Learning
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 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 2021 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: remote sensing based prediction of species, and (iv) SnakeCLEF: Automatic Snake Species Identification with Country-Level Focus.
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https://inria.hal.science/hal-03415990/documentView
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UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#4 Quality Education
#15 Life on Land

Source: SDGs in the Output

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