GeoPlant: Spatial Plant Species Prediction Dataset

TL;DR

GeoPlant introduces a comprehensive high-resolution European plant species dataset combining diverse ecological and remote sensing data, alongside a benchmark for species distribution modeling to advance biodiversity monitoring and conservation.

Contribution

The paper presents a novel large-scale, multimodal dataset for SDMs, integrating heterogeneous ecological and satellite data, and provides an accessible benchmark to foster research in plant species prediction.

Findings

Dataset includes over 10,000 species and 5 million records.

Provides multimodal data: satellite images, environmental rasters, and survey records.

Benchmark on Kaggle with strong baseline models.

Abstract

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.