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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- image-classification
+- feature-extraction
+- zero-shot-classification
+language:
+- en
+tags:
+- biodiversity
+- biology
+- computer-vision
+- multimodal
+- self-supervised-learning
+- florida
+- plants
+pretty_name: DeepEarth Central Florida Native Plants
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+    - split: train
+      path: observations.parquet
+    - split: test
+      path: observations.parquet
+---
+
+# DeepEarth Central Florida Native Plants Dataset v0.2.0
+
+<p align="center">
+  <img src="https://github.com/deepearth/assets/deepearth-logo.png" alt="DeepEarth" width="200"/>
+</p>
+
+## 🌿 Dataset Summary
+
+A comprehensive multimodal biodiversity 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 biodiversity monitoring and species distribution modeling.
+
+### 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
+
+```python
+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 | Species identifier |
+| `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 | Number of images |
+| `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
+```python
+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
+```python
+# 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
+```python
+# 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
+```python
+# 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 **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.
+
+## 📚 Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@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 biodiversity monitoring.
+
+## 🔗 Related Resources
+
+- [DeepEarth Project](https://github.com/deepearth)
+- [V-JEPA Model](https://github.com/facebookresearch/jepa)
+- [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3)
+- [GBIF Portal](https://www.gbif.org)
+
+## ⚠️ Ethical Considerations
+
+- All observations are from public citizen science platforms
+- No endangered species location data is included at fine resolution
+- Please respect local regulations when using location data
+
+## 📈 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
+
+---
+*Dataset prepared by the DeepEarth team for advancing multimodal biodiversity research.*