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

arXiv: https://arxiv.org/abs/2509.25816 · HTML: https://mlsanity.com/paper/2509.25816/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [Multi-Label Learning from Single Positive Labels](https://mlsanity.com/paper/2106.09708/) (2021)
- [Deep Multi-Species Embedding](https://mlsanity.com/paper/1609.09353/) (2016)

## Built on by (most cited first, 2 of 2)

- [GeoPl@ntNet: A Platform for Exploring Essential Biodiversity Variables](https://mlsanity.com/paper/2511.13790/) (2025)
- [A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models](https://mlsanity.com/paper/2604.13240/) (2026)

## Abstract (arXiv)

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.