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AI is increasingly central to understanding and managing biodiversity and ecosystems. Since 2011, the LifeCLEF lab has provided large-scale benchmarks that stimulate progress in multimodal species recognition, ecological prediction, and knowledge extraction. The 2026 edition expands this scope with five complementary challenges spanning visual, acoustic, and textual data: (i) AnimalCLEF: discovery and re-identification of individual animals, (ii) BirdCLEF+: multi-taxonomic species recognition in complex soundscapes, (iii) FathomNetCLEF: detection of marine species in underwater imagery under positive-unlabeled constraints, (iv) PestCLEF: extraction of information on plant pests from heterogeneous textual sources, (v) PlantCLEF: multi-species plant identification in quadrat images. Together, these challenges address critical dimensions of biodiversity science and ecosystem management, while fostering collaboration between AI researchers, ecologists, and practitioners. This paper provides an overview of the LifeCLEF 2026 lab and its tasks, outlining their motivation, data, and evaluation methodology to guide participants and inform the wider research community.

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LifeCLEF 2026 Teaser: AI Challenges for Biodiversity Understanding and Ecosystem Management

  • 会议论文

  • 发表日期：

    2026年

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  • Alexis Joly,
  • Lukáš Picek,
  • Stefan Kahl,
  • Hervé Goëau,
  • Lukáš Adam,
  • Robert Bossy,
  • Kostas Papafitsoros,
  • Vojtěch Čermák,
  • Holger Klinck,
  • Willem-Pier Vellinga,
  • Robert Planqué,
  • Tom Denton,
  • Laura Chrobak,
  • Kevin Barnard,
  • Claire Nédellec,
  • Louise Deléger,
  • Marine Courtin,
  • Giulio Martellucci,
  • Fabrice Vinatier,
  • Pierre Bonnet

摘要

AI is increasingly central to understanding and managing biodiversity and ecosystems. Since 2011, the LifeCLEF lab has provided large-scale benchmarks that stimulate progress in multimodal species recognition, ecological prediction, and knowledge extraction. The 2026 edition expands this scope with five complementary challenges spanning visual, acoustic, and textual data: (i) AnimalCLEF: discovery and re-identification of individual animals, (ii) BirdCLEF+: multi-taxonomic species recognition in complex soundscapes, (iii) FathomNetCLEF: detection of marine species in underwater imagery under positive-unlabeled constraints, (iv) PestCLEF: extraction of information on plant pests from heterogeneous textual sources, (v) PlantCLEF: multi-species plant identification in quadrat images. Together, these challenges address critical dimensions of biodiversity science and ecosystem management, while fostering collaboration between AI researchers, ecologists, and practitioners. This paper provides an overview of the LifeCLEF 2026 lab and its tasks, outlining their motivation, data, and evaluation methodology to guide participants and inform the wider research community.