The GeoLifeCLEF 2020 Dataset
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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.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1; 2025: 3.
8 papers.
- 2021When Does Contrastive Visual Representation Learning Work?cited by 92
- 2022EarthNets: Empowering AI in Earth Observationcited by 40
- 2021Species Distribution Modeling for Machine Learning Practitioners: A Reviewcited by 21
- 2023LD-SDM: Language-Driven Hierarchical Species Distribution Modelingcited by 4
- 2024DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity Datasetscited by 4
- 2025BioCube: A Multimodal Dataset for Biodiversity Researchcited by 1
- 2025Predicting butterfly species presence from satellite imagery using soft contrastive regularisationcited by 0
- 2025BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Modelscited by 0
- 2025BATIS: Bayesian Approaches for Targeted Improvement of Species Distribution Models22 Oct 2025
- 2025BioCube: A Multimodal Dataset for Biodiversity Research16 May 2025
- 2025Predicting butterfly species presence from satellite imagery using soft contrastive regularisation14 May 2025
- 2024DivShift: Exploring Domain-Specific Distribution Shifts in Large-Scale, Volunteer-Collected Biodiversity Datasets17 Oct 2024
- 2023LD-SDM: Language-Driven Hierarchical Species Distribution Modeling13 Dec 2023
- 2022EarthNets: Empowering AI in Earth Observation10 Oct 2022
- 2021Species Distribution Modeling for Machine Learning Practitioners: A Review3 Jul 2021
- 2021When Does Contrastive Visual Representation Learning Work?12 May 2021