Overview of LifeCLEF 2024: Challenges on Species Distribution Prediction and Identification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43973170" target="_blank" >RIV/49777513:23520/24:43973170 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-71908-0_9" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-71908-0_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-71908-0_9" target="_blank" >10.1007/978-3-031-71908-0_9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Overview of LifeCLEF 2024: Challenges on Species Distribution Prediction and Identification

Popis výsledku v původním jazyce

Biodiversity monitoring using machine learning and AI-based approaches is becoming increasingly popular. It allows for providing detailed information on species distribution and ecosystem health at a large scale and contributes to informed decision-making on environmental protection. Species identification based on images and sounds, in particular, is invaluable for facilitating biodiversity monitoring efforts and enabling prompt conservation actions to protect threatened and endangered species. The multiplicity of methods developed, however, makes it important to evaluate their performance on realistic datasets and using standardized evaluation protocols. The LifeCLEF lab has been setting up such evaluations since 2011, encouraging machine learning researchers to work on this topic and promoting the adoption of the technologies developed by stakeholders. The 2024 edition proposes five data-oriented challenges related to the identification and prediction of biodiversity: (i) BirdCLEF: bird call identification in soundscapes, (ii) FungiCLEF: revisiting fungi species recognition beyond 0-1 cost, (iii) GeoLifeCLEF: remote sensing based prediction of species, (iv) PlantCLEF: Multi-species identification in vegetation plot images, and (v) SnakeCLEF: revisiting snake species identification in medically important scenarios. This paper overviews the motivation, methodology, and main outcomes of those five challenges.

Název v anglickém jazyce

Overview of LifeCLEF 2024: Challenges on Species Distribution Prediction and Identification

Popis výsledku anglicky

Biodiversity monitoring using machine learning and AI-based approaches is becoming increasingly popular. It allows for providing detailed information on species distribution and ecosystem health at a large scale and contributes to informed decision-making on environmental protection. Species identification based on images and sounds, in particular, is invaluable for facilitating biodiversity monitoring efforts and enabling prompt conservation actions to protect threatened and endangered species. The multiplicity of methods developed, however, makes it important to evaluate their performance on realistic datasets and using standardized evaluation protocols. The LifeCLEF lab has been setting up such evaluations since 2011, encouraging machine learning researchers to work on this topic and promoting the adoption of the technologies developed by stakeholders. The 2024 edition proposes five data-oriented challenges related to the identification and prediction of biodiversity: (i) BirdCLEF: bird call identification in soundscapes, (ii) FungiCLEF: revisiting fungi species recognition beyond 0-1 cost, (iii) GeoLifeCLEF: remote sensing based prediction of species, (iv) PlantCLEF: Multi-species identification in vegetation plot images, and (v) SnakeCLEF: revisiting snake species identification in medically important scenarios. This paper overviews the motivation, methodology, and main outcomes of those five challenges.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Experimental IR Meets Multilinguality, Multimodality, and Interaction. Lecture Notes in Computer Science

ISBN

978-3-031-71907-3

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

25

Strana od-do

183-207

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Grenoble

Datum konání akce

9. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001336411000009