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Participation of LIRMM / Inria to the GeoLifeCLEF 2020 challenge

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Abstract

This paper describes the methods that we have implemented in the context of the GeoLifeCLEF 2020 machine learning challenge. The goal of this challenge is to advance the state-of-the-art in location-based species recommendation on a very large dataset of 1.9 million species observations, paired with high-resolution remote sensing imagery, land cover data, and altitude. We provide a detailed description of the algorithms and methodology, developed by the LIRMM / Inria team, in order to facilitate the understanding and reproducibility of the obtained results.
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Dates and versions

hal-02989084 , version 1 (05-11-2020)

Identifiers

  • HAL Id : hal-02989084 , version 1

Cite

Benjamin Deneu, Maximilien Servajean, Pierre Bonnet, François Munoz, Alexis Joly. Participation of LIRMM / Inria to the GeoLifeCLEF 2020 challenge. 2020. ⟨hal-02989084⟩
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