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

Authors
  • Botella, Christophe
  • Servajean, Maximilien
  • Bonnet, Pierre
  • Joly, Alexis
Publication Date
Sep 09, 2019
Source
HAL
Keywords
Language
English
License
Unknown
External links

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 [email protected] 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.

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