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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20205 - Automation and control systems

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Article name in the collection

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

Number of pages

25

Pages from-to

183-207

Publisher name

Springer

Place of publication

Cham

Event location

Grenoble

Event date

Sep 9, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001336411000009