nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

Overview of LifeCLEF 2020: A System-Oriented Evaluation of Automated Species Identification and Species Distribution Prediction

verfasst von : Alexis Joly, Hervé Goëau, Stefan Kahl, Benjamin Deneu, Maximillien Servajean, Elijah Cole, Lukáš Picek, Rafael Ruiz de Castañeda, Isabelle Bolon, Andrew Durso, Titouan Lorieul, Christophe Botella, Hervé Glotin, Julien Champ, Ivan Eggel, Willem-Pier Vellinga, Pierre Bonnet, Henning Müller

Erschienen in: Experimental IR Meets Multilinguality, Multimodality, and Interaction

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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 2020 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: snake identification based on image and geographic location.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications

Nächstes Kapitel Overview of LiLAS 2020 – Living Labs for Academic Search

https://www.cbd.int/.

http://www.lifeclef.org/.

http://www.imageclef.org/.

https://www.aicrowd.com.

http://portal.idigbio.org/portal/search.

https://explore.recolnat.org/search/botanique/type=index.

https://www.xeno-canto.org/.

National Agriculture Image Program, https://www.fsa.usda.gov.

https://geoservices.ign.fr.

http://osr-cesbio.ups-tlse.fr/~oso/posts/2017-03-30-carte-s2-2016/.

https://lpdaac.usgs.gov/products/srtmgl1v003/.

Most of the Stanford team’s methods were based on deep neural networks, but the authors informed us that they encounter convergence issues resulting in performance poorer than expected.

Bai, J., Chen, C., Chen, J.: Xception based system for bird sound detection. In: CLEF Working Notes 2020, CLEF: Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 2020 (2020)

Bloch, L., et al.: Combination of image and location information for snake species identification using object detection and efficientnets. In: CLEF Working Notes 2020, CLEF: Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 2020 (2020)

Bolon, I., et al.: Identifying the snake: first scoping review on practices of communities and healthcare providers confronted with snakebite across the world. PLoS One 15(3), e0229989 (2020)

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_8CrossRef

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

de Castañeda, R.R., et al.: Snakebite and snake identification: empowering neglected communities and health-care providers with AI. Lancet Digit. Health 1(5), e202–e203 (2019)CrossRef

Chulif, S., Chang, Y.L.: Cross-domain plant identification on French Guyana Flora: neuon submission to LifeCLEF 2020 plant. In: CLEF Working Notes 2020, CLEF: Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 2020 (2020)

Clementino, T., Colonna, J.G.: Using triplet loss to bird species recognition on BirdCLEF 2020. In: CLEF Working Notes 2020, CLEF: Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 2020 (2020)

Cole, E., et al.: The GeoLifeCLEF 2020 dataset. arXiv preprint arXiv:2004.04192 (2020)

10.

Deneu, B., et al.: Overview of LifeCLEF location-based species prediction task 2020 (GeoLifeCLEF). In: CLEF task overview 2020, CLEF: Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 2020 (2020)

11.

Deneu, B., Servajean, M., Joly, A.: Participation of LIRMM/Inria to the GeoLifeCLEF 2020 challenge. In: CLEF Working Notes 2020, CLEF: Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 2020 (2020)

12.

Sprengel, E., Jaggi, M., Kilcher, Y., Hofmann, T.: Audio based bird species identification using deep learning techniques. In: CLEF Working Notes 2016, CLEF: Conference and Labs of the Evaluation Forum, Évora, Portugal, September 2016 (2016)

13.

Evans, J.S., Murphy, M.A., Holden, Z.A., Cushman, S.A.: Modeling species distribution and change using random forest. In: Drew, C., Wiersma, Y., Huettmann, F. (eds.) Predictive Species and Habitat Modeling in Landscape Ecology, pp. 139–159. Springer, New York (2011). https://doi.org/10.1007/978-1-4419-7390-0_8CrossRef

14.

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

15.

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

16.

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

17.

Goëau, H., Bonnet, P., Joly, A.: Plant identification in an open-world (LifeCLEF 2016). In: CLEF Task Overview 2016, CLEF: Conference and Labs of the Evaluation Forum, September 2016, Évora, Portugal (2016)

18.

Goëau, H., Bonnet, P., Joly, A.: Plant identification based on noisy web data: the amazing performance of deep learning (LifeCLEF 2017). In: CLEF Task Overview 2017, CLEF: Conference and Labs of the Evaluation Forum, Dublin, Ireland, September 2017 (2017)

19.

Goëau, H., Bonnet, P., Joly, A.: Overview of ExpertLifeCLEF 2018: how far automated identification systems are from the best experts? In: CLEF Task Overview 2018, CLEF: Conference and Labs of the Evaluation Forum, Avignon, France, September 2018 (2018)

20.

Goëau, H., Bonnet, P., Joly, A.: Overview of LifeCLEF plant identification task 2019: diving into data deficient tropical countries. In: CLEF Task Overview 2019, CLEF: Conference and Labs of the Evaluation Forum, Lugano, Switzerland, September 2019 (2019)

21.

Goëau, H., Bonnet, P., Joly, A.: Overview of LifeCLEF plant identification task 2020. In: CLEF Task Overview 2020, CLEF: Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 2020 (2020)

22.

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

23.

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

24.

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

25.

Goëau, H., Glotin, H., Planqué, R., Vellinga, W.P., Stefan, Kahl, J.A.: Overview of BirdCLEF 2018: monophone vs. soundscape bird identification. In: CLEF Task Overview 2018, CLEF: Conference and Labs of the Evaluation Forum, Avignon, France, September 2018 (2018)

26.

Goëau, H., Joly, A., Bonnet, P.: LifeCLEF plant identification task 2015. In: CLEF Task Overview 2015, CLEF: Conference and Labs of the Evaluation Forum, Toulouse, France, September 2015 (2015)

27.

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)

28.

Goëau, H., et al.: The LifeCLEF 2014 plant images identification task. In: CLEF Task Overview 2014, CLEF: Conference and Labs of the Evaluation Forum, Sheffield, United Kingdom, September 2014 (2014)

29.

Hengl, T., et al.: SoilGrids250m: global gridded soil information based on machine learning. PLoS One 12(2), e0169748 (2017)CrossRef

30.

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A.: Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol.: J. Roy. Meteorol. Soc. 25(15), 1965–1978 (2005)CrossRef

31.

Homer, C., et al.: Completion of the 2011 national land cover database for the conterminous united states - representing a decade of land cover change information. Photogram. Eng. Rem. Sens. 81(5), 345–354 (2015)

32.

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

33.

Joly, A., et al.: Overview of LifeCLEF 2018: a large-scale evaluation of species identification and recommendation algorithms in the era of AI. In: Bellot, P., et al. (eds.) CLEF 2018. LNCS, vol. 11018, pp. 247–266. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98932-7_24CrossRef

34.

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. Lecture Notes in Computer Science, vol. 11696, pp. 387–401. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28577-7_29. https://hal.umontpellier.fr/hal-02281455CrossRef

35.

Joly, A., et al.: LifeCLEF 2016: multimedia life species identification challenges. In: Fuhr, N., et al. (eds.) CLEF 2016. LNCS, vol. 9822, pp. 286–310. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44564-9_26. https://hal.archives-ouvertes.fr/hal-01373781CrossRef

36.

Joly, A., et al.: LifeCLEF 2017 lab overview: multimedia species identification challenges. In: Jones, G.J.F., et al. (eds.) CLEF 2017. LNCS, vol. 10456, pp. 255–274. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65813-1_24. https://hal.archives-ouvertes.fr/hal-01629191CrossRef

37.

Joly, A., et al.: LifeCLEF 2014: multimedia life species identification challenges. In: Kanoulas, E., et al. (eds.) CLEF 2014. LNCS, vol. 8685, pp. 229–249. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11382-1_20. https://hal.inria.fr/hal-01075770CrossRef

38.

Joly, A., et al.: LifeCLEF 2015: multimedia life species identification challenges. In: Mothe, J., et al. (eds.) CLEF 2015. Lecture Notes in Computer Science, vol. 9283, pp. 462–483. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24027-5_46CrossRef

39.

Kahl, S., et al.: Overview of BirdCLEF 2020: bird sound recognition in complex acoustic environments. In: CLEF Task Overview 2020, CLEF: Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 2020 (2020)

40.

Kahl, S., Stöter, F.R., Glotin, H., Planqué, R., Vellinga, W.P., Joly, A.: Overview of BirdCLEF 2019: large-scale bird recognition in soundscapes. In: CLEF Task Overview 2019, CLEF: Conference and Labs of the Evaluation Forum, Lugano, Switzerland, September 2019 (2019)

41.

Kahl, S., Wilhelm-Stein, T., Klinck, H., Kowerko, D., Eibl, M.: Recognizing birds from sound - the 2018 BirdCLEF baseline system. arXiv preprint arXiv:1804.07177 (2018)

42.

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)

43.

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

44.

Moorthy, G.K.: Impact of pretrained networks for snake species classification. In: CLEF Working Notes 2020, CLEF: Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 2020 (2020)

45.

Motiian, S., Jones, Q., Iranmanesh, S., Doretto, G.: Few-shot adversarial domain adaptation. In: Advances in Neural Information Processing Systems, pp. 6670–6680 (2017)

46.

Krishna, N.H., Ram Kaushik, R., R.M.: Plant species identification using transfer learning - PlantCLEF 2020. In: CLEF Working Notes 2020, CLEF: Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 2020 (2020)

47.

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

48.

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, Thessaloniki, Greece, September 2020 (2020)

49.

Picek, L., Sulc, M., Matas, J.: Recognition of the Amazonian flora by inception networks with test-time class prior estimation. In: CLEF Working Notes 2019, CLEF: Conference and Labs of the Evaluation Forum, Lugano, Switzerland, September 2019 (2019)

50.

Roberts, D.R., et al.: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40(8), 913–929 (2017)CrossRef

51.

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

52.

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

53.

Van Horn, G., et al.: The inaturalist species classification and detection dataset. In: CVPR (2018)

54.

Villacis, J., Goëau, H., Bonnet, P., Mata-Montero, E., Joly, A.: Domain adaptation in the context of herbarium collections: a submission to PlantCLEF 2020. In: CLEF Working Notes 2020, CLEF: Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 2020 (2020)

55.

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

56.

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

57.

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, 52 (2013)

58.

Zhang, Y., Davison, B.D.: Adversarial consistent learning on partial domain adaptation of PlantCLEF 2020 challenge. In: CLEF Working Notes 2020, CLEF: Conference and Labs of the Evaluation Forum, Thessaloniki, Greece, September 2020 (2020)

Titel: Overview of LifeCLEF 2020: A System-Oriented Evaluation of Automated Species Identification and Species Distribution Prediction
verfasst von: Alexis Joly
Hervé Goëau
Stefan Kahl
Benjamin Deneu
Maximillien Servajean
Elijah Cole
Lukáš Picek
Rafael Ruiz de Castañeda
Isabelle Bolon
Andrew Durso
Titouan Lorieul
Christophe Botella
Hervé Glotin
Julien Champ
Ivan Eggel
Willem-Pier Vellinga
Pierre Bonnet
Henning Müller
Verlag: Springer International Publishing
Buch: Experimental IR Meets Multilinguality, Multimodality, and Interaction
Print ISBN: 978-3-030-58218-0

Electronic ISBN: 978-3-030-58219-7

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-58219-7_23

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"