Overview of GeoLifeCLEF 2024: Species composition prediction with high spatial resolution at continental scale using remote sensing
2024
Picek, Lukáš | Botella, Christophe | Servajean, Maximilien | Leblanc, César | Palard, Rémi | Larcher, Théo | Deneu, Benjamin | Marcos, Diego | Estopinan, Joaquim | Bonnet, Pierre | Joly, Alexis | Institut National de Recherche en Informatique et en Automatique (Inria) | Centre National de la Recherche Scientifique (CNRS) | Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [Occitanie])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM) | European Commission;EC;UE;http://dx.doi.org/10.13039/501100000780 | Faggioli Guglielmo (ed.) | Ferro Nicola (ed.) | Galuščáková Petra (ed.) | García Seco de Herrera Alba (ed.)
Source Agritrop Cirad (https://agritrop.cirad.fr/613023/) * Autres projets (id;sigle;titre): 101060639;MAMBO;(EU) Modern Approaches to the Monitoring of BiOdiversity// 101060693;GUARDEN;(EU) safeGUARDing biodivErsity aNd critical ecosystem services across sectors and scales//
显示更多 [+] 显示较少 [-]International audience
显示更多 [+] 显示较少 [-]英语. Understanding the spatiotemporal distribution of species is a cornerstone of ecology and conservation. Pairing species observations with geographic and environmental predictors allows us to model the relationship between an environment and the species present at a given location. In light of that, we organize an annual competition, GeoLifeCLEF, which focuses on benchmarking and advancing state-of-the-art species distribution modeling using available bioclimatic and remote sensing data. The GeoLifeCLEF 2024 dataset spans across Europe and encompasses most of its flora. The species observation data comprises over 5 million Presence-Only (PO) occurrences and approximately 90 thousand Presence-Absence (PA) surveys. Those data are paired with various high-resolution rasters, including remote sensing imagery, land cover, and elevation, and are combined with coarse-resolution data such as climate, soil, and human footprint variables. In this paper, we present (i) an overview of the GeoLifeCLEF 2024 competition, (ii) a description of the provided data, (iii) an overview of approaches used by the participating teams, and (iv) the main results analysis.
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