Overview of GeoLifeCLEF 2023: Species Composition Prediction with High Spatial Resolution at Continental Scale Using Remote Sensing

• URL: http://arxiv.org/abs/2509.25816v1
• Date: Tue, 30 Sep 2025 05:49:16 GMT
• Title: Overview of GeoLifeCLEF 2023: Species Composition Prediction with High Spatial Resolution at Continental Scale Using Remote Sensing
• Authors: Christophe Botella, Benjamin Deneu, Diego Marcos, Maximilien Servajean, Theo Larcher, Cesar Leblanc, Joaquim Estopinan, Pierre Bonnet, Alexis Joly,
• Abstract summary: We organized an open machine learning challenge called GeoLifeCLEF 2023.<n>The training dataset comprised of 5 million plant species distributed across Europe.<n>We evaluated models ability to predict species in 22 thousand small plots based on standardized surveys.
• Score: 9.66382598562254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the spatio-temporal distribution of species is a cornerstone of ecology and conservation. By pairing species observations with geographic and environmental predictors, researchers can model the relationship between an environment and the species which may be found there. To advance the state- of-the-art in this area with deep learning models and remote sensing data, we organized an open machine learning challenge called GeoLifeCLEF 2023. The training dataset comprised 5 million plant species observations (single positive label per sample) distributed across Europe and covering most of its flora, high-resolution rasters: remote sensing imagery, land cover, elevation, in addition to coarse-resolution data: climate, soil and human footprint variables. In this multi-label classification task, we evaluated models ability to predict the species composition in 22 thousand small plots based on standardized surveys. This paper presents an overview of the competition, synthesizes the approaches used by the participating teams, and analyzes the main results. In particular, we highlight the biases faced by the methods fitted to single positive labels when it comes to the multi-label evaluation, and the new and effective learning strategy combining single and multi-label data in training.

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