Abstract
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 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: location-based prediction of species based on environmental and occurrence data and (iv) SnakeCLEF: image-based snake identification.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
AICrowd. https://www.aicrowd.com/
CEUR-WS. http://ceur-ws.org/
CLEF 2021. https://clef2021.clef-initiative.eu/
Convention on Biodiversity. https://www.cbd.int/
ImageCLEF. http://www.imageclef.org/
LifeCLEF. http://www.lifeclef.org/
LifeCLEF 2021. https://www.imageclef.org/LifeCLEF2021
The FAIR Data Principles. https://www.force11.org/group/fairgroup/fairprinciples
Bonnet, P., et al.: Plant identification: experts vs. machines in the era of deep learning. In: Joly, A., Vrochidis, S., Karatzas, K., Karppinen, A., Bonnet, P. (eds.) Multimedia Tools and Applications for Environmental & Biodiversity Informatics. MSA, pp. 131–149. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76445-0_8
Cai, J., Ee, D., Pham, B., Roe, P., Zhang, J.: Sensor network for the monitoring of ecosystem: Bird species recognition. In: 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, 2007. ISSNIP 2007 (2007). https://doi.org/10.1109/ISSNIP.2007.4496859
Cole, E., et al.: The geolifeclef 2020 dataset. arXiv preprint arXiv:2004.04192 (2020)
Gaston, K.J., O’Neill, M.A.: Automated species identification: why not? Philosophical Transactions of the Royal Society of London B: Biological Sciences 359(1444), 655–667 (2004)
Ghazi, M.M., Yanikoglu, B., Aptoula, E.: Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235, 228–235 (2017)
Glotin, H., LeCun, Y., Artiéres, T., Mallat, S., Tchernichovski, O., Halkias, X.: Proceedings of the Neural Information Processing Scaled for Bioacoustics, from Neurons to Big Data. NIPS International Conference on Tahoe USA (2013). http://sabiod.org/nips4b
Goeau, H., Bonnet, P., Joly, A.: Plant identification based on noisy web data: the amazing performance of deep learning (LifeCLEF 2017). In: CLEF 2017-Conference and Labs of the Evaluation Forum, pp. 1–13 (2017)
Goëau, H., et al.: The ImageCLEF 2013 plant identification task. In: CLEF. Valencia, Spain (2013)
Goëau, H., et al.: The ImageCLEF 2011 plant images classification task. In: CLEF 2011 (2011)
Goëau, H., et al.: ImageCLEF2012 plant images identification task. In: CLEF 2012, Rome (2012)
Goëau, H., et al.: The imageclef plant identification task 2013. In: Proceedings of the 2nd ACM International Workshop on Multimedia analysis for Ecological Data, pp. 23–28. ACM (2013)
ICML International Conference: Proceedings of the 1st workshop on Machine Learning for Bioacoustics - ICML4B (2013). http://sabiod.univ-tln.fr
Joly, A., et al.: Interactive plant identification based on social image data. Ecol. Inform. 23, 22–34 (2014)
Lee, D.J., Schoenberger, R.B., Shiozawa, D., Xu, X., Zhan, P.: Contour matching for a fish recognition and migration-monitoring system. In: Optics East, pp. 37–48. International Society for Optics and Photonics (2004)
Lee, S.H., Chan, C.S., Remagnino, P.: Multi-organ plant classification based on convolutional and recurrent neural networks. IEEE Trans. Image Process. 27(9), 4287–4301 (2018)
Picek, L., Ruiz De Castaneda, R., Durso, A.M., Sharada, P.: Overview of the snakeclef 2020: automatic snake species identification challenge. In: CLEF (Working Notes) (2020)
Towsey, M., Planitz, B., Nantes, A., Wimmer, J., Roe, P.: A toolbox for animal call recognition. Bioacoustics 21(2), 107–125 (2012)
Trifa, V.M., Kirschel, A.N., Taylor, C.E., Vallejo, E.E.: Automated species recognition of antbirds in a Mexican rainforest using hidden Markov models. J. Acoust. Soc. Am. 123, 2424 (2008)
Van Horn, G., et al.: The inaturalist species classification and detection dataset. In: CVPR (2018)
Wäldchen, J., Mäder, P.: Machine learning for image based species identification. Methods Ecol. Evol. 9(11), 2216–2225 (2018)
Wäldchen, J., Rzanny, M., Seeland, M., Mäder, P.: Automated plant species identification–trends and future directions. PLoS Comput. Biol. 14(4) (2018)
Yu, X., Wang, J., Kays, R., Jansen, P.A., Wang, T., Huang, T.: Automated identification of animal species in camera trap images. In: EURASIP Journal on Image and Video Processing (2013)
Acknowledgements
This work is supported in part by the SEAMED PACA project, the SMILES project (ANR-18-CE40-0014), and an NSF Graduate Research Fellowship (DGE-1745301). This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 863463 (Cos4Cloud project).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Joly, A. et al. (2021). LifeCLEF 2021 Teaser: Biodiversity Identification and Prediction Challenges. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_70
Download citation
DOI: https://doi.org/10.1007/978-3-030-72240-1_70
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72239-5
Online ISBN: 978-3-030-72240-1
eBook Packages: Computer ScienceComputer Science (R0)