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Species Distribution Modeling based on aerial images and environmental features with Convolutional Neural Networks

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Abstract

Predicting which species are likely to be observed at a given location is an important issue both from a scientific point of view and for citizens interested in biodiversity. The aim of the GeoLifeCLEF challenge is to predict the presence of around 17K plant and animal species using 1.6M geo-localized observations from France and the US. Beyond GPS coordinates, additional covariates are provided for each observation: remote sensing imagery, land cover data, altitude data, bioclimatic data and pedologic data. We tested two simple approaches making use of every covariate available: (i) training separate models from them, testing them separately, and, (ii) combining those models together in the simplest manner, i.e., averaging their predicted scores. These simple methods allowed us to reach the fourth position on the private leaderboard of the competition. We also managed to improve our score after the end of the challenge by implementing a multi-modal convolutional neural network with separated features extractors.
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Dates and versions

hal-03817452 , version 1 (17-10-2022)

Licence

Attribution - CC BY 4.0

Identifiers

  • HAL Id : hal-03817452 , version 1

Cite

César Leblanc, Alexis Joly, Titouan Lorieul, Maximilien Servajean, Pierre Bonnet. Species Distribution Modeling based on aerial images and environmental features with Convolutional Neural Networks. CLEF 2022 - Conference and Labs of the Evaluation Forum, Guglielmo Faggioli; Nicola Ferro; Allan Hanbury; Martin Potthast, Sep 2022, Bologne, Italy. pp.2123-2150. ⟨hal-03817452⟩
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