gbif-plants-raw

A large-scale dataset of 96.1 million research-grade plant observations sourced from iNaturalist Open Data and aligned with GBIF taxonomy. Each row contains species metadata, taxonomic identifiers, geolocation, event timing, dataset source info, and a direct image URL.

This dataset is designed for large-scale image classification, biodiversity modelling, and pretraining work.

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Dataset Summary

This dataset aggregates all research-grade Plantae observations from iNaturalist and exports them into a flat, machine-friendly Parquet format.

Each record includes:

• Taxonomy: species, genus, family names + GBIF IDs
• Geolocation: latitude, longitude
• Event metadata: event date, dataset source
• Image metadata: direct image URL (iNat CDN), license info
• Local identifiers: GBIF occurrence IDs, dataset keys

No images are stored in this dataset. Only URLs and metadata are provided.

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Use Cases

• Large-scale plant species classification
• Vision transformer pretraining (ViT, ConvNeXt, etc.)
• Weakly-supervised learning using URLs
• Region-aware or habitat-aware plant ID models
• Training LoRA adapters for specific plant subsets
• Ecological modelling using species + geolocation
• Dataset bootstrapping for downstream fine-tuning tasks

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Dataset Structure

Total rows: 96,100,000+ Format: Parquet Split: train (single split)

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Features

Column	Type	Description
gbif_id	string	GBIF occurrence ID
species_id	string	GBIF species ID
genus_id	string	GBIF genus ID
family_id	string	GBIF family ID
species_name	string	Scientific species name
genus_name	string	Scientific genus name
family_name	string	Scientific family name
lat	string	Latitude
lon	string	Longitude
event_date	string	Observation event timestamp
dataset_key	string	GBIF dataset key
dataset_name	string	Dataset name (usually iNaturalist research-grade)
basis_of_record	string	Observation type
image_url	string	Direct image URL (iNaturalist Open Data)
license_raw	string	License URL for the media
rights_holder	string	Name of the copyright holder

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Licensing

All media follows the original iNaturalist Open Data licensing provided by contributors. Each row includes license_raw and rights_holder.

You must comply with the specific Creative Commons license associated with each image URL. This dataset itself (metadata only) is released under CC0, but images are NOT included and are NOT CC0.

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How to Load

Python (HF Datasets)

from datasets import load_dataset

ds = load_dataset("juppy44/gbif-plants-raw", split="train", streaming=True)

for row in ds.take(5):
    print(row["species_name"], row["image_url"])

Polars

import polars as pl

df = pl.read_parquet("shard_0000.parquet")
print(df.head())

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Notes

• Some observations may contain outdated or deprecated GBIF taxonomy entries.
• Image URLs point to iNaturalist CDN mirrors and may expire in rare cases.
• No filtering by image quality, angle, or duplicates has been applied.
• Future versions may include cleaned subsets or image-validated variants.

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Citation

If you use this dataset, please cite:

iNaturalist Open Data. https://www.inaturalist.org/pages/open-data
GBIF: The Global Biodiversity Information Facility. https://www.gbif.org/
juppy44. gbif-plants-raw (2025). https://huggingface.co/datasets/juppy44/gbif-plants-raw

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Contact / Contributions

Open to collaboration on:

• cleaned or taxon-specific subsets
• embedding-based deduplication
• full image dataset export
• pretraining versions for ViT / ConvNeXt
• LoRA adapter training pipelines

Feel free to open issues or discussions on the Hugging Face repo.