Abstract
Building accurate knowledge of the identity, the geographic distribution and the evolution of living species is essential for a sustainable development of humanity, as well as for biodiversity conservation. Unfortunately, such basic information is often only partially available for professional stakeholders, teachers, scientists and citizens, and often incomplete for ecosystems that possess the highest diversity. In this context, an ultimate ambition is to set up innovative information systems relying on the automated identification and understanding of living organisms as a means to engage massive crowds of observers and boost the production of biodiversity and agro-biodiversity data. The LifeCLEF 2019 initiative proposes three data-oriented challenges related to this vision, in the continuity of the previous editions but with several consistent novelties intended to push the boundaries of the state-of-the-art in several research directions. This paper describes the methodology of the conducted evaluations as well as the synthesis of the main results and lessons learned.
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Acknowledgements
We would like to thank very warmly Julien Engel, Rémi Girault, Jean-François Molino and the two other expert botanists who agreed to participate in the task on plant identification. We also we would like to thank the University of Montpellier and the Floris’Tic project (ANRU) who contributed to the funding of the 2019-th edition of LifeCLEF.
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Joly, A. et al. (2019). Overview of LifeCLEF 2019: Identification of Amazonian Plants, South & North American Birds, and Niche Prediction. In: Crestani, F., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2019. Lecture Notes in Computer Science(), vol 11696. Springer, Cham. https://doi.org/10.1007/978-3-030-28577-7_29
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DOI: https://doi.org/10.1007/978-3-030-28577-7_29
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