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Participation of LIRMM / Inria to the GeoLifeCLEF 2020 challenge

Benjamin Deneu 1 Maximilien Servajean 2, 3 Pierre Bonnet 4 François Munoz 5 Alexis Joly 1
1 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
3 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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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https://hal.inria.fr/hal-02989084
Contributor : Alexis Joly Connect in order to contact the contributor
Submitted on : Thursday, November 5, 2020 - 9:01:26 AM
Last modification on : Wednesday, November 3, 2021 - 6:12:38 AM
Long-term archiving on: : Saturday, February 6, 2021 - 6:25:37 PM

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  • HAL Id : hal-02989084, version 1

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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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