Overview of GeoLifeCLEF 2019: plant species prediction using environment and animal occurrences

Christophe Botella 1, 2 Maximilien Servajean 3 Pierre Bonnet 2 Alexis Joly 4
1 INRA - Institut National de la Recherche Agronomique
2 UMR AMAP - Botanique et Modélisation de l'Architecture des Plantes et des Végétations
3 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
4 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The GeoLifeCLEF challenge aim to evaluate location-based species recommendation algorithms through open and perennial datasets in a reproducible way. It offers a ground for large-scale geographic species prediction using cross-kingdom occurrences and spatialized environmental data. The main novelty of the 2019 campaign over the previous one is the availability of new occurrence datasets: (i) automatically identified plant occurrences coming from the popular Pl@ntnet platform and (ii) animal occurrences coming from the GBIF platform. This paper presents an overview of the resources and assessment of the GeoLifeCLEF 2019 task, synthesizes the approaches used by the participating groups and analyzes the main evaluation results. We highlight new successful approaches relevant for community modeling like models learning to predict occurrences from many biological groups and methods weighting occurrences based on species infrequency.
Keywords : Methods comparison Benchmark Species Distribution Models Evaluation Presence-only data Biodiversity Model performance LifeCLEF Prediction Predictive power Environmental data Species recommendation
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Submitted on : Monday, July 22, 2019 - 10:46:10 AM
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INRA | ZENITH | LIRMM | AGROPOLIS | CIRAD | IRD | MIPS | INRIA | UNIV-MONTPELLIER | B3ESTE | AMAP | ADVANSE

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Christophe Botella, Maximilien Servajean, Pierre Bonnet, Alexis Joly. Overview of GeoLifeCLEF 2019: plant species prediction using environment and animal occurrences. CLEF 2019 - Conference and Labs of the Evaluation Forum, Sep 2019, Lugano, Switzerland. ⟨hal-02190170⟩

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