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2022 | OriginalPaper | Buchkapitel

LifeCLEF 2022 Teaser: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction

verfasst von : Alexis Joly, Hervé Goëau, Stefan Kahl, Lukáš Picek, Titouan Lorieul, Elijah Cole, Benjamin Deneu, Maximilien Servajean, Andrew Durso, Isabelle Bolon, Hervé Glotin, Robert Planqué, Willem-Pier Vellinga, Holger Klinck, Tom Denton, Ivan Eggel, Pierre Bonnet, Henning Müller, Milan Šulc

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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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, animals and fungi is hindering the aggregation of new data and knowledge. Identifying and naming living organisms 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 2022 edition proposes five data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: very large-scale plant identification, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: remote sensing based prediction of species, (iv) SnakeCLEF: Snake Species Identification in Medically Important scenarios, and (v) FungiCLEF: Fungi recognition from images and metadata.

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Vorheriges Kapitel ImageCLEF 2022: Multimedia Retrieval in Medical, Nature, Fusion, and Internet Applications

Nächstes Kapitel The ChEMU 2022 Evaluation Campaign: Information Extraction in Chemical Patents

https://www.cbd.int/.

http://www.lifeclef.org/.

http://www.imageclef.org/.

https://github.com/plantnet/gbif-dl.

https://www.gbif.org/.

e.g. some species might be oversampled or undersampled.

AICrowd. https://www.aicrowd.com/

Kaggle. https://www.kaggle.com/

LifeCLEF (2022). https://www.imageclef.org/LifeCLEF2022

Affouard, A., Goeau, H., Bonnet, P., Lombardo, J.C., Joly, A.: Pl@ntnet app in the era of deep learning. In: 5th International Conference on Learning Representations (ICLR 2017), 24–26 April 2017, Toulon, France (2017)

Goëau, H., 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_8CrossRef

Cai, J., Ee, D., Pham, B., Roe, P., Zhang, J.: Sensor network for the monitoring of ecosystem: Bird species recognition. In: Intelligent Sensors, Sensor Networks and Information, 2007. ISSNIP 2007. 3rd International Conference on (2007). https://doi.org/10.1109/ISSNIP.2007.4496859

Gaston, K.J., O’Neill, M.A.: Automated species identification: why not? Philos. Trans. Royal Soc. London B: Biol. Sci. 359(1444), 655–667 (2004)CrossRef

Ghazi, M.M., Yanikoglu, B., Aptoula, E.: Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235, 228–235 (2017)CrossRef

Glotin, H., Clark, C., LeCun, Y., Dugan, P., Halkias, X., Sueur, J.: Proceeding 1st workshop on Machine Learning for Bioacoustics - ICML4B. ICML, Atlanta USA (2013). http://sabiod.org/ICML4B2013_book.pdf

10.

Goëau, H., et al.: The imageclef 2013 plant identification task. In: CLEF task Overview 2013, CLEF: Conference and Labs of the Evaluation Forum, Sep. 2013, Valencia, Spain. Valencia (2013)

11.

Goëau, H., et al.: The imageclef 2011 plant images classification task. In: CLEF task Overview 2011, CLEF: Conference and Labs of the Evaluation Forum, Sep. 2011, Amsterdam, Netherlands. (2011)

12.

Goëau, H., et al.: Imageclef 2012 plant images identification task. In: CLEF Task Overview 2012, CLEF: Conference and Labs of the Evaluation Forum, Sep. 2012, Rome, Italy. Rome (2012)

13.

Joly, A., et al.: Interactive plant identification based on social image data. Ecol. Inf. 23, 22–34 (2014)CrossRef

14.

Joly, A., et al.: Overview of LifeCLEF 2018: a large-scale evaluation of species identification and recommendation algorithms in the era of ai. In: Jones, G.J., et al. (eds.) CLEF: Cross-Language Evaluation Forum for European Languages. Experimental IR Meets Multilinguality, Multimodality, and Interaction, vol. LNCS. Springer, Avigon, France (Sep 2018)

15.

Joly, A., et al.: Overview of LifeCLEF 2019: Identification of Amazonian Plants, South & North American Birds, and Niche Prediction. In: Crestani, F., et al. (eds.) CLEF 2019 - Conference and Labs of the Evaluation Forum. Experimental IR Meets Multilinguality, Multimodality, and Interaction, vol. LNCS, pp. 387–401. Lugano, Switzerland (Sep 2019). https://doi.org/10.1007/978-3-030-28577-7_29, https://hal.umontpellier.fr/hal-02281455

16.

Joly, A., et al.: LifeCLEF 2016: Multimedia Life Species Identification Challenges. In: Fuhr, N., et al. (eds.) CLEF: Cross-Language Evaluation Forum. Experimental IR Meets Multilinguality, Multimodality, and Interaction, vol. LNCS, pp. 286–310. Springer, Évora, Portugal (Sep 2016). https://doi.org/10.1007/978-3-319-44564-9_26, https://hal.archives-ouvertes.fr/hal-01373781

17.

Joly, A., et al.: LifeCLEF 2017 Lab Overview: Multimedia Species Identification Challenges. In: Jones, G.J., et al. (eds.) CLEF: Cross-Language Evaluation Forum. Experimental IR Meets Multilinguality, Multimodality, and Interaction, vol. LNCS, pp. 255–274. Springer, Dublin, Ireland (Sep 2017). https://doi.org/10.1007/978-3-319-65813-1_24, https://hal.archives-ouvertes.fr/hal-01629191

18.

Joly, A., et al.: LifeCLEF 2014: Multimedia Life Species Identification Challenges. In: CLEF: Cross-Language Evaluation Forum. Information Access Evaluation. Multilinguality, Multimodality, and Interaction, vol. LNCS, pp. 229–249. Springer International Publishing, Sheffield, United Kingdom (Sep 2014). https://doi.org/10.1007/978-3-319-11382-1_20, https://hal.inria.fr/hal-01075770

19.

Joly, A., et al.: Lifeclef 2015: multimedia life species identification challenges. In: Experimental IR Meets Multilinguality, Multimodality, and Interaction, pp. 462–483. Springe, Chem (2015)

20.

Joly, A., et al.: Overview of lifeclef 2020: a system-oriented evaluation of automated species identification and species distribution prediction. In: International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 342–363. Springer, Chem (2020)

21.

Joly, A., et al.: Overview of lifeclef 2021: an evaluation of machine-learning based species identification and species distribution prediction. In: International Conference of the Cross-Language Evaluation Forum for European Languages, pp. 371–393. Springer, Chem (2021)

22.

Koh, L.P., Dunn, R.R., Sodhi, N.S., Colwell, R.K., Proctor, H.C., Smith, V.S.: Species coextinctions and the biodiversity crisis. Science 305(5690), 1632–1634 (2004)CrossRef

23.

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)

24.

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)MathSciNetCrossRef

25.

NIPS International Conference on Neural Information Processing Scaled for Bioacoustics, from Neurons to Big Data (2013). http://sabiod.org/nips4b

26.

Norouzzadeh, M.S., Morris, D., Beery, S., Joshi, N., Jojic, N., Clune, J.: A deep active learning system for species identification and counting in camera trap images. Methods Ecol. Evol. 12(1), 150–161 (2021)CrossRef

27.

Picek, L., Ruiz De Castañeda, R., Durso, A.M., Sharada, P.M.: Overview of the snakeclef 2020: Automatic snake species identification challenge. In: CLEF task overview 2020, CLEF: Conference and Labs of the Evaluation Forum, Sep. 2020, Thessaloniki, Greece (2020)

28.

Picek, L., Sulc, M., Matas, J., Heilmann-Clausen, J., Jeppesen, T., Læssøe, T., Frøslev, T.: Danish fungi 2020 - not just another image recognition dataset. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (2022)

29.

Picek, L., Durso, A.M., Ruiz De Castañeda, R., Bolon, I.: Overview of SnakeCLEF 2021: Automatic snake species identification with country-level focus. In: Working Notes of CLEF 2021 - Conference and Labs of the Evaluation Forum (2021)

30.

Towsey, M., Planitz, B., Nantes, A., Wimmer, J., Roe, P.: A toolbox for animal call recognition. Bioacoustics 21(2), 107–125 (2012)CrossRef

31.

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)CrossRef

32.

Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., Belongie, S.: The inaturalist species classification and detection dataset. CVPR (2018)

33.

Villon, S., Mouillot, D., Chaumont, M., Subsol, G., Claverie, T., Villéger, S.: A new method to control error rates in automated species identification with deep learning algorithms. Sci. Reports 10(1), 1–13 (2020)

34.

Wäldchen, J., Mäder, P.: Machine learning for image based species identification. Methods Ecol. Evol. 9(11), 2216–2225 (2018)CrossRefMATH

35.

Wäldchen, J., Rzanny, M., Seeland, M., Mäder, P.: Automated plant species identification-trends and future directions. PLoS Comput. Biol. 14(4), e1005993 (2018)CrossRef

36.

Yu, X., Wang, J., Kays, R., Jansen, P.A., Wang, T., Huang, T.: Automated identification of animal species in camera trap images. EURASIP J. Image Video Process. 2013(1), 1–10 (2013). https://doi.org/10.1186/1687-5281-2013-52CrossRef

Titel: LifeCLEF 2022 Teaser: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction
verfasst von: Alexis Joly
Hervé Goëau
Stefan Kahl
Lukáš Picek
Titouan Lorieul
Elijah Cole
Benjamin Deneu
Maximilien Servajean
Andrew Durso
Isabelle Bolon
Hervé Glotin
Robert Planqué
Willem-Pier Vellinga
Holger Klinck
Tom Denton
Ivan Eggel
Pierre Bonnet
Henning Müller
Milan Šulc
Verlag: Springer International Publishing
Buch: Advances in Information Retrieval
Print ISBN: 978-3-030-99738-0

Electronic ISBN: 978-3-030-99739-7

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-99739-7_49

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