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Overview of GeoLifeCLEF 2021: Predicting species distribution from 2 million remote sensing images

Abstract : 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 research in this area, a large-scale machine learning competition called GeoLifeCLEF 2021 was organized. It relied on a dataset of 1.9 million observations from 31K species mainly of animals and plants. These observations were paired with high-resolution remote sensing imagery, land cover data, and altitude, in addition to traditional lowresolution climate and soil variables. The main goal of the challenge was to better understand how to leverage remote sensing data to predict the presence of species at a given location. 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.
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Contributor : Yannick Brohard Connect in order to contact the contributor
Submitted on : Friday, September 24, 2021 - 9:54:54 AM
Last modification on : Thursday, October 14, 2021 - 2:12:07 PM


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Distributed under a Creative Commons Attribution 4.0 International License


  • HAL Id : hal-03353487, version 1


Titouan Lorieul, Elijah Cole, Benjamin Deneu, Maximilien Servajean, Pierre Bonnet, et al.. Overview of GeoLifeCLEF 2021: Predicting species distribution from 2 million remote sensing images. Working Notes of CLEF 2021 - Conference and Labs of the Evaluation Forum, Guglielmo Faggioli; Nicola Ferro; Alexis Joly; Maria Maistro; Florina Piroi, Sep 2021, Bucarest, Romania. pp.1451-1462. ⟨hal-03353487⟩



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