You are here

LifeCLEF 2016

Primary tabs



  • If you have not registered in the past or have changed affiliation you can register for 2016 here.
  • For people already registered in the past, you can transfer your registration details to CLEF 2016 here.

Once registered, data access details can be found at the ImageCLEF/LifeCLEF registration system -> Collections .


  • 15th Nov 2015: registration opens for all LifeCLEF tasks (until 30.04.2016)
  • Nov 2015 - Feb 2016: training data release (depends on the task)
  • 1st March 2016: test data release
  • 1st May 2016: deadline for submission of runs by the participants
  • 15th May 2016: release of processed results by the task organizers
  • 25th May 2016: deadline for submission of working notes papers by the participants
  • 17th June 2016: notification of acceptance of the working notes papers
  • 1st July 2016: camera ready working notes papers
  • 5th-8th Sept 2016: CLEF 2016 Évora, Portugal

Working notes

Submitting a working note with the full description of the methods used in each run is mandatory. Any run that could not be reproduced thanks to its description in the working notes might be removed from the official publication of the results. Working notes are published within CEUR-WS proceedings, resulting in an assignment of an individual DOI (URN) and an indexing by many bibliography systems including DBLP. According to the CEUR-WS policies, a light review of the working notes will be conducted by LifeCLEF organizing committee to ensure quality. As an illustration, LifeCLEF 2015 working notes (task overviews and participant working notes) can be found within CLEF 2015 CEUR-WS proceedings.


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. A noticeable cause and consequence of this sparse knowledge is that identifying living plants or animals is usually impossible for the general public, and often a difficult task for professionals, such as farmers, fish farmer or foresters and even also for the naturalists and specialists themselves. This taxonomic gap was actually identified as one of the main ecological challenges to be solved during the Rio’s United Nations Conference in 1992.


In this context, an ultimate ambition is to set up a world-scale collaborative workflow relying on the automated identification and understanding of living organisms as a mean to engage massive crowds of observers and boost the production of biodiversity and agro-biodiversity data. Whereas existing initiatives consider only a part of these objectives (e.g. building identification tools, or building biodiversity data sharing platforms, or building new crowdsourcing models), the ground-breaking concept underlying LifeCLEF is to initiate a long-term positive feedback loop as illustrated in in the above Figure. The release of innovative machine learning tools running on a variety of mobile and connected devices first allow engaging much more nature observers than current biodiversity-related participatory sensing initiatives or specialized social networks. This consequently boosts the production of biodiversity data (in particular for the species in the long-tail) and finally increases the performances of the automatic recognition tools that are trained on the produced data.

The LifeCLEF 2016 lab proposes three data-oriented challenges related to this vision, in the continuity of the two previous editions of the lab, but with several consistent novelties intended to push the boundaries of the state-of-the-art in several research directions at the frontier of information retrieval, machine learning and knowledge engineering including:

  • Large Scale Classification
  • Weakly-supervised and open-set classification
  • Transfer learning & fine-grained classification
  • Crowdsourcing models and algorithms
  • Interactive and mobile search
  • Scene understanding
  • Focused crawling and record linkage
  • More concretely, the lab is organized around three tasks:

    The LifeCLEF lab has some intersections with the ImageCLEF lab but also has some consistent differences:

  • whereas the global objective of ImageCLEF is to benchmark automatic annotation and indexing technologies, the LifeCLEF lab is fully open to non automatic approaches including crowdsourcing and/or interactive-based annotation solutions. Even purely manual runs are welcome since they allow to compare machine-learning based approaches to human performance (e.g. depending on the expertise as in ).
  • LifeCLEF also deals with more various modalities including audio recordings, videos and a variety of meta-data such as types of sounds, plant organs annotations, geo-loc, observation date, observation id, authors, etc.
  • whereas ImageCLEF works in a pretty well controlled environment in terms of usable training data and targeted labels, LifeCLEF works in a more open world with (i) a focus on open-set classification problems within the plant task and (ii), the possibility in all tasks to use any external training data including non audio-visual contents such as species distribution maps, environmental variables, etc.
  • Contact


    • Alexis Joly, INRIA Sophia-Antipolis - ZENITH team, LIRMM, University of Montpellier, France, alexis.joly(replace-by-an-arrobe)
    • Henning Müller, University of Applied Sciences Western Switzerland in Sierre, Switzerland, henning.mueller(replace-by-an-arrobe)


    • Hervé Glotin, University of Toulon, France, glotin(replace-by-an-arrobe)
    • Hervé Goëau, Inria, France.
    • Willem-Pier Vellinga, Xeno-Canto foundation for nature sounds, The Netherlands, wp(replace-by-an-arrobe)
    • Alexis Joly, INRIA Sophia-Antipolis - ZENITH team, LIRMM, University of Montpellier, France, alexis.joly(replace-by-an-arrobe)


    • Hervé Goëau, Inria, France.
    • Alexis Joly, INRIA Sophia-Antipolis - ZENITH team, LIRMM, University of Montpellier, France, alexis.joly(replace-by-an-arrobe)
    • Pierre Bonnet, Cirad – AMAP, Montpellier, France, pierre.bonnet(replace-by-an-arrobe)


    • Concetto Spampinato, University of Catania, Italy, cspampin(replace-by-an-arrobe)
    • Simone Palazzo, University of Catania, Italy, simone.palazzo(replace-by-an-arrobe)


    • Ivan Eggel, University of Applied Sciences Western Switzerland, Sierre, Switzerland, ivan.eggel(replace-by-an-arrobe)

    Floris'tic      Inria          CNRS      Xeno-Canto           Sabiod

    feedback-loop-lifeclef2016.png135.2 KB
    CNRSfr.jpg20.85 KB
    INRIA_LOGO.jpg157.76 KB