# GeoPlant: Spatial Plant Species Prediction Dataset

arXiv: https://arxiv.org/abs/2408.13928 · HTML: https://mlsanity.com/paper/2408.13928/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 65 references that are in the map.
- First posted on arXiv 25 Aug 2024.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Regression as Classification: Influence of Task Formulation on Neural Network Features](https://mlsanity.com/paper/2211.05641/) (2022)
- [On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning](https://mlsanity.com/paper/2401.02989/) (2024)

## Built on by (most cited first, 16 of 16)

- [TaxaBind: A Unified Embedding Space for Ecological Applications](https://mlsanity.com/paper/2411.00683/) (2024)
- [WildSAT: Learning Satellite Image Representations from Wildlife Observations](https://mlsanity.com/paper/2412.14428/) (2024)
- [MMEarth-Bench: Global Model Adaptation via Multimodal Test-Time Training](https://mlsanity.com/paper/2602.06285/) (2026)
- [GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classification](https://mlsanity.com/paper/2505.12513/) (2025)
- [Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach](https://mlsanity.com/paper/2505.17637/) (2025)
- [Feedforward Few-shot Species Range Estimation](https://mlsanity.com/paper/2502.14977/) (2025)
- [MaskSDM with Shapley values to improve flexibility, robustness, and explainability in species distribution modeling](https://mlsanity.com/paper/2503.13057/) (2025)
- [Building Bridges between Regression, Clustering, and Classification](https://mlsanity.com/paper/2502.02996/) (2025)
- [MALPOLON: A Framework for Deep Species Distribution Modeling](https://mlsanity.com/paper/2409.18102/) (2024)
- [Mapping biodiversity at very-high resolution in Europe](https://mlsanity.com/paper/2504.05231/) (2025)
- [BioCube: A Multimodal Dataset for Biodiversity Research](https://mlsanity.com/paper/2505.11568/) (2025)
- [CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations](https://mlsanity.com/paper/2508.06704/) (2025)
- [Applying the maximum entropy principle to neural networks enhances multi-species distribution models](https://mlsanity.com/paper/2412.19217/) (2024)
- [GeoPl@ntNet: A Platform for Exploring Essential Biodiversity Variables](https://mlsanity.com/paper/2511.13790/) (2025)
- [Audio-to-Image Bird Species Retrieval without Audio-Image Pairs via Text Distillation](https://mlsanity.com/paper/2602.00681/) (2026)
- [SAGE: A sampling-aware global evaluation benchmark for species distribution modeling](https://mlsanity.com/paper/2609.31082/) (2026)

## Abstract (arXiv)

The difficulty of monitoring biodiversity at fine scales and over large areas limits ecological knowledge and conservation efforts. To fill this gap, Species Distribution Models (SDMs) predict species across space from spatially explicit features. Yet, they face the challenge of integrating the rich but heterogeneous data made available over the past decade, notably millions of opportunistic species observations and standardized surveys, as well as multimodal remote sensing data. In light of that, we have designed and developed a new European-scale dataset for SDMs at high spatial resolution (10--50m), including more than 10k species (i.e., most of the European flora). The dataset comprises 5M heterogeneous Presence-Only records and 90k exhaustive Presence-Absence survey records, all accompanied by diverse environmental rasters (e.g., elevation, human footprint, and soil) traditionally used in SDMs. In addition, it provides Sentinel-2 RGB and NIR satellite images with 10 m resolution, a 20-year time series of climatic variables, and satellite time series from the Landsat program. In addition to the data, we provide an openly accessible SDM benchmark (hosted on Kaggle), which has already attracted an active community and a set of strong baselines for single predictor/modality and multimodal approaches. All resources, e.g., the dataset, pre-trained models, and baseline methods (in the form of notebooks), are available on Kaggle, allowing one to start with our dataset literally with two mouse clicks.