Overview of GeoLifeCLEF 2022: Predicting species presence from multi-modal remote sensing, bioclimatic and pedologic data - Archive ouverte HAL Access content directly
Conference Papers Year : 2022

Overview of GeoLifeCLEF 2022: Predicting species presence from multi-modal remote sensing, bioclimatic and pedologic data

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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 2022 was organized. It relied on a dataset of 1.6 million observations from 17K species 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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Dates and versions

lirmm-03793595 , version 1 (01-10-2022)

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Attribution - CC BY 4.0

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  • HAL Id : lirmm-03793595 , version 1

Cite

Titouan Lorieul, Elijah Cole, Benjamin Deneu, Maximilien Servajean, Pierre Bonnet, et al.. Overview of GeoLifeCLEF 2022: Predicting species presence from multi-modal remote sensing, bioclimatic and pedologic data. CLEF 2022 - Conference and Labs of the Evaluation Forum, Sep 2022, Bologne, Italy. pp.1940-1956. ⟨lirmm-03793595⟩
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