# The GeoLifeCLEF 2020 Dataset

arXiv: https://arxiv.org/abs/2004.04192 · HTML: https://mlsanity.com/paper/2004.04192/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [When Does Contrastive Visual Representation Learning Work?](https://mlsanity.com/paper/2105.05837/) (2021)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [Species Distribution Modeling for Machine Learning Practitioners: A Review](https://mlsanity.com/paper/2107.10400/) (2021)
- [LD-SDM: Language-Driven Hierarchical Species Distribution Modeling](https://mlsanity.com/paper/2312.08334/) (2023)
- [DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity Datasets](https://mlsanity.com/paper/2410.19816/) (2024)
- [BioCube: A Multimodal Dataset for Biodiversity Research](https://mlsanity.com/paper/2505.11568/) (2025)
- [Predicting butterfly species presence from satellite imagery using soft contrastive regularisation](https://mlsanity.com/paper/2505.09306/) (2025)
- [BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models](https://mlsanity.com/paper/2510.19749/) (2025)

## Abstract (arXiv)

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.