Overview of LifeCLEF location-based species prediction task 2020 (GeoLifeCLEF)

Benjamin Deneu (1) , Titouan Lorieul (1) , Elijah Cole (2) , Maximilien Servajean (3) , Christophe Botella (4) , Pierre Bonnet (4) , Alexis Joly (1)

1 ZENITH - Scientific Data Management
2 CALTECH - California Institute of Technology
3 ADVANSE - ADVanced Analytics for data SciencE
4 UMR AMAP - Botanique et Modélisation de l'Architecture des Plantes et des Végétations

Understanding the geographic distribution of species is a key concern in conservation. By pairing species occurrences with environmental features, researchers can model the relationship between an environment and the species which may be found there. To advance the state-of-the-art in this area, a large-scale machine learning competition called GeoLifeCLEF 2020 was organized. It relied on a dataset of 1.9 million species observations paired with high-resolution remote sensing imagery, land cover data, and altitude, in addition to traditional low-resolution climate and soil variables. This paper presents an overview of the competition , synthesizes the approaches used by the participating groups, and analyzes the main results. In particular, we highlight the ability of remote sensing imagery and convolutional neural networks to improve predictive performance, complementary to traditional approaches.

Mots clés: LifeCLEF; biodiversity; environmental data; species dis- tribution; evaluation; benchmark; species distribution models; methods comparison; presence-only data; model performance; prediction; predic- tive power

Domaines: Intelligence artificielle [cs.AI]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Multimédia [cs.MM]; Apprentissage [cs.LG]; Modélisation et simulation; Biodiversité et Ecologie; Environnement et Société

Benjamin Deneu, Titouan Lorieul, Elijah Cole, Maximilien Servajean, Christophe Botella, et al.. Overview of LifeCLEF location-based species prediction task 2020 (GeoLifeCLEF). CLEF 2020 - Conference and Labs of the Evaluation Forum, Sep 2020, Thessaloniki, Greece. ⟨hal-02989077⟩