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The GeoLifeCLEF 2020 Dataset

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arxiv 2004.04192 v1 pith:AG6FRCUK submitted 2020-04-08 cs.CV cs.LGstat.ML

Elijah Cole , Benjamin Deneu , Titouan Lorieul , Maximilien Servajean , Christophe Botella , Dan Morris , Nebojsa Jojic , Pierre Bonnet
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Alexis Joly
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classification cs.CVcs.LGstat.ML
keywords speciesdatasetgeolifeclefadditionadvanceaimsaltitudearea
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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 facilitate research in this area, we present the GeoLifeCLEF 2020 dataset, which consists of 1.9 million species observations paired with high-resolution remote sensing imagery, land cover data, and altitude, in addition to traditional low-resolution climate and soil variables. We also discuss the GeoLifeCLEF 2020 competition, which aims to use this dataset to advance the state-of-the-art in location-based species recommendation.

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  1. CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations

    cs.LG 2025-08 unverdicted novelty 6.0 of 10

    CISO is a deep learning model that conditions species distribution predictions on incomplete observations of other species, improving performance across plants, birds, and butterflies.

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