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

Lorieul Titouan, Cole Elijah, Deneu Benjamin, Servajean Maximilien, Bonnet Pierre, Joly Alexis. 2021. Overview of GeoLifeCLEF 2021: Predicting species distribution from 2 million remote sensing images. In : CEUR Workshop Proceedings. Faggioli Guglielmo (ed.), Ferro Nicola (ed.), Joly Alexis (ed.), Maistro Maria (ed.), Piroi Florina (ed.). s.l. : CEUR-WS, 1451-1462. (CEUR Workshop Proceedings, 2936) Conference and Labs of the Evaluation Forum (CLEF 2021), Bucharest, Roumanie, 21 Septembre 2021/24 Septembre 2021.

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Résumé : 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 low- resolution 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.

Mots-clés libres : LifeCLEF, Evaluation, Benchmark, Biodiversity, Presence-only data, Environmental data, Remote sensing imagery, Species distribution, Species distribution models

Agences de financement européennes : European Commission

Agences de financement hors UE : Agence Nationale de la Recherche

Programme de financement européen : H2020

Projets sur financement : (FRA) Institut Convergences en Agriculture Numérique, (EU) Co-designed Citizen Observatories Services for the EOS-Cloud

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