deepearth/central-florida-native-plants

DeepEarth Central Florida Native Plants Dataset v0.2.0

🌿 Dataset Summary

A comprehensive multimodal dataset featuring 33,665 observations of 232 native plant species from Central Florida. This dataset combines citizen science observations with state-of-the-art vision and language embeddings for advancing multimodal self-supervised ecological intelligence research.

Key Features

• 🌍 Spatiotemporal Coverage: Complete GPS coordinates and timestamps for all observations
• 🖼️ Multimodal: 31,136 observations with images, 7,113 with vision embeddings
• 🧬 Language Embeddings: DeepSeek-V3 embeddings for all 232 species
• 👁️ Vision Embeddings: V-JEPA-2 self-supervised features (6.5M dimensions)
• 📊 Rigorous Splits: Spatiotemporal train/test splits for robust evaluation

📦 Dataset Structure

observations.parquet         # Main dataset (500MB)
vision_index.parquet        # Vision embeddings index
vision_embeddings/          # Vision features (50GB total)
├── embeddings_000000.parquet
├── embeddings_000001.parquet
└── ... (159 files)

🚀 Quick Start

from datasets import load_dataset
import pandas as pd

# Load main dataset
dataset = load_dataset("deepearth/central-florida-plants")

# Access data
train_data = dataset['train']
print(f"Training samples: {len(train_data)}")
print(f"Features: {train_data.features}")

# Load vision embeddings (download required due to size)
vision_index = pd.read_parquet("vision_index.parquet")
vision_data = pd.read_parquet("vision_embeddings/embeddings_000000.parquet")

📊 Data Fields

Each observation contains:

Field	Type	Description
gbif_id	int64	Unique GBIF occurrence ID
taxon_id	string	GBIF taxon ID
taxon_name	string	Scientific species name
latitude	float	GPS latitude
longitude	float	GPS longitude
year	int	Observation year
month	int	Observation month
day	int	Observation day
hour	int	Observation hour (nullable)
minute	int	Observation minute (nullable)
second	int	Observation second (nullable)
image_urls	List[string]	URLs to observation images
num_images	int	Relative image number in GBIF occurrence
has_vision	bool	Vision embeddings available
vision_file_indices	List[int]	Indices to vision files
language_embedding	List[float]	7,168-dim DeepSeek-V3 embedding
split	string	train/spatial_test/temporal_test

🔄 Data Splits

The dataset uses rigorous spatiotemporal splits:

{ "train": 30935, "temporal_test": 2730 }

• Temporal Test: All 2025 observations (future generalization)
• Spatial Test: 5 non-overlapping geographic regions
• Train: Remaining observations

🤖 Embeddings

Language Embeddings (DeepSeek-V3)

• Dimensions: 7,168
• Source: Scientific species descriptions
• Coverage: All 232 species

Vision Embeddings (V-JEPA-2)

• Dimensions: 6,488,064 values per embedding
• Structure: 8 temporal frames × 24×24 spatial patches × 1408 features
• Model: Vision Transformer Giant with self-supervised pretraining
• Coverage: 7,113 images
• Storage: Flattened arrays in parquet files (use provided utilities to reshape)

💡 Usage Examples

Working with V-JEPA 2 Embeddings

import numpy as np
import ast

# Load vision embedding
vision_df = pd.read_parquet("vision_embeddings/embeddings_000000.parquet")
row = vision_df.iloc[0]

# Reshape from flattened to 4D structure
embedding = row['embedding']
original_shape = ast.literal_eval(row['original_shape'])  # [4608, 1408]

# First to 2D: (4608 patches, 1408 features)
embedding_2d = embedding.reshape(original_shape)

# Then to 4D: (8 temporal, 24 height, 24 width, 1408 features)
embedding_4d = embedding_2d.reshape(8, 24, 24, 1408)

# Get specific temporal frame (0-7)
frame_0 = embedding_4d[0]  # Shape: (24, 24, 1408)

# Get mean embedding for image-level tasks
image_embedding = embedding_4d.mean(axis=(0, 1, 2))  # Shape: (1408,)

Species Distribution Modeling

# Filter observations for a specific species
species_data = dataset.filter(lambda x: x['taxon_name'] == 'Quercus virginiana')

# Use spatiotemporal data for distribution modeling
coords = [(d['latitude'], d['longitude']) for d in species_data]

Multimodal Learning

# Combine vision and language embeddings
for sample in dataset:
    if sample['has_vision']:
        lang_emb = sample['language_embedding']
        vision_idx = sample['vision_file_indices'][0]
        # Load corresponding vision embedding
        vision_emb = load_vision_embedding(vision_idx)

Zero-shot Species Classification

# Use language embeddings for zero-shot classification
species_embeddings = {
    species['taxon_name']: species['language_embedding']
    for species in dataset.unique('taxon_name')
}

📄 License

This dataset is released under the MIT License.

📚 Citation

If you use this dataset, please cite:

@dataset{deepearth_cf_plants_2024,
  title={DeepEarth Central Florida Native Plants: A Multimodal Biodiversity Dataset},
  author={DeepEarth Team},
  year={2024},
  version={0.2.0},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/deepearth/central-florida-plants}
}

🌟 Acknowledgments

We thank all citizen scientists who contributed observations through iNaturalist and GBIF. This dataset was created as part of the DeepEarth initiative for multimodal self-supervised ecological intelligence research.

🔗 Related Resources

• DeepEarth Project
• V-JEPA Model
• DeepSeek-V3
• GBIF Portal

📈 Dataset Statistics

• Total Size: ~51 GB
• Main Dataset: 500 MB
• Vision Embeddings: 50 GB
• Image URLs: 31,136 total images referenced
• Temporal Range: 2019-2025
• Geographic Scope: Central Florida, USA

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Dataset prepared by the DeepEarth team for advancing multimodal self-supervised ecological intelligence research.