Datasets:

mpg-ranch
/

drone-lsr

@@ -1,177 +0,0 @@
----
-license: cc-by-4.0
-task_categories:
-- feature-extraction
-- image-to-image
-language:
-- en
-tags:
-- remote-sensing
-- aerial-imagery
-- orthomosaic
-- lighting-invariance
-- semantic-stability
-- vision-encoder
-- time-series
-- dinov2
-- dinov3
-- embeddings
-- multi-config
-pretty_name: Light Stable Semantics
-size_categories:
-- n<1K
----
-# Light Stable Semantics Dataset
-## Dataset Description
-This dataset contains aerial orthomosaic tiles captured at three different times of day (10:00, 12:00, and 15:00). The dataset is organized into three configurations: `default` (raw images + canopy height), `dinov2_base` (DINOv2 embeddings), and `dinov3_sat` (DINOv3 embeddings). All configurations share consistent train/test splits with matching tile identifiers for cross-referencing. The dataset is designed for training vision encoders that maintain consistent feature representations despite changes in illumination, with applications in remote sensing and environmental monitoring.
-## Dataset Configurations
-The dataset is organized into three configurations, each serving different research needs:
-### Configuration: `default`
-Raw imagery and environmental data for direct analysis:
-| Feature | Type | Shape | Description |
-|---------|------|--------|-------------|
-| `idx` | string | - | Tile identifier in format `{ROW}_{COL}` for geographic referencing |
-| `image_t0` | Image | 1024×1024×3 | Morning capture at 10:00 AM (time=1000) |
-| `image_t1` | Image | 1024×1024×3 | Noon capture at 12:00 PM (time=1200) |
-| `image_t2` | Image | 1024×1024×3 | Afternoon capture at 3:00 PM (time=1500) |
-| `canopy_height` | int32 | [1024, 1024] | Canopy height grid in centimeters from canopy height model |
-### Configuration: `dinov2_base`
-Pre-computed DINOv2 Base (ViT-B/14) embeddings:
-| Feature | Type | Shape | Description |
-|---------|------|--------|-------------|
-| `idx` | string | - | Tile identifier matching other configurations |
-| `cls_t0` | float32 | [768] | DINOv2 CLS token (global features) for morning image |
-| `cls_t1` | float32 | [768] | DINOv2 CLS token (global features) for noon image |
-| `cls_t2` | float32 | [768] | DINOv2 CLS token (global features) for afternoon image |
-| `patch_t0` | float32 | [256, 768] | DINOv2 patch tokens (16×16 spatial grid) for morning image |
-| `patch_t1` | float32 | [256, 768] | DINOv2 patch tokens (16×16 spatial grid) for noon image |
-| `patch_t2` | float32 | [256, 768] | DINOv2 patch tokens (16×16 spatial grid) for afternoon image |
-### Configuration: `dinov3_sat`
-Pre-computed DINOv3 Large (ViT-L/16) embeddings with satellite pretraining:
-| Feature | Type | Shape | Description |
-|---------|------|--------|-------------|
-| `idx` | string | - | Tile identifier matching other configurations |
-| `cls_t0` | float32 | [1024] | DINOv3 CLS token (global features) for morning image |
-| `cls_t1` | float32 | [1024] | DINOv3 CLS token (global features) for noon image |
-| `cls_t2` | float32 | [1024] | DINOv3 CLS token (global features) for afternoon image |
-| `patch_t0` | float32 | [196, 1024] | DINOv3 patch tokens (14×14 spatial grid) for morning image |
-| `patch_t1` | float32 | [196, 1024] | DINOv3 patch tokens (14×14 spatial grid) for noon image |
-| `patch_t2` | float32 | [196, 1024] | DINOv3 patch tokens (14×14 spatial grid) for afternoon image |
-**Notes:**
-- Canopy height values represent centimeters above ground; missing data is encoded as `-2147483648`
-- All configurations use consistent 80%/20% train/test splits with matching `idx` values
-- Patch tokens represent spatial features in different grid resolutions: 16×16 (DINOv2) vs 14×14 (DINOv3)
-## Usage Example
-```python
-from datasets import load_dataset
-# Load specific configurations
-dataset_default = load_dataset("mpg-ranch/light-stable-semantics", "default")
-dataset_dinov2 = load_dataset("mpg-ranch/light-stable-semantics", "dinov2_base")
-dataset_dinov3 = load_dataset("mpg-ranch/light-stable-semantics", "dinov3_sat")
-# Access raw imagery and canopy height
-sample_default = dataset_default['train'][0]
-morning_image = sample_default['image_t0']      # RGB image
-noon_image = sample_default['image_t1']         # RGB image
-afternoon_image = sample_default['image_t2']    # RGB image
-canopy_height = sample_default['canopy_height'] # Height grid in cm
-tile_id = sample_default['idx']                 # Geographic identifier
-# Access DINOv2 embeddings (same tile via matching idx)
-sample_dinov2 = dataset_dinov2['train'][0]
-dinov2_cls_morning = sample_dinov2['cls_t0']     # Global features (768-dim)
-dinov2_patches_morning = sample_dinov2['patch_t0'] # Spatial features (256×768)
-# Access DINOv3 embeddings (same tile via matching idx)
-sample_dinov3 = dataset_dinov3['train'][0]
-dinov3_cls_morning = sample_dinov3['cls_t0']     # Global features (1024-dim)
-dinov3_patches_morning = sample_dinov3['patch_t0'] # Spatial features (196×1024)
-# Verify consistent tile identifiers across configurations
-assert sample_default['idx'] == sample_dinov2['idx'] == sample_dinov3['idx']
-# Access test sets for evaluation
-test_default = dataset_default['test'][0]
-test_dinov2 = dataset_dinov2['test'][0]
-test_dinov3 = dataset_dinov3['test'][0]
-```
-## Pre-computed Embeddings
-The dataset includes pre-computed embeddings from two state-of-the-art vision transformers:
-### DINOv2 Base (`facebook/dinov2-base`)
-- **Architecture**: Vision Transformer Base with 14×14 patch size
-- **CLS Tokens**: 768-dimensional global feature vectors capturing scene-level semantics
-- **Patch Tokens**: 256×768 arrays (16×16 spatial grid) encoding local features
-- **Training**: Self-supervised learning on natural images
-### DINOv3 Large (`facebook/dinov3-vitl16-pretrain-sat493m`)
-- **Architecture**: Vision Transformer Large with 16×16 patch size
-- **CLS Tokens**: 1024-dimensional global feature vectors capturing scene-level semantics
-- **Patch Tokens**: 196×1024 arrays (14×14 spatial grid) encoding local features
-- **Training**: Self-supervised learning with satellite imagery pretraining
-**Purpose**: Enable efficient training and analysis without requiring on-the-fly feature extraction, while providing comparison between natural image and satellite-pretrained models.
-## Dataset Information
-- **Location**: Lower Partridge Alley, MPG Ranch, Montana, USA
-- **Survey Date**: November 7, 2024
-- **Coverage**: 620 complete tile sets (80% train / 20% test split via seeded random sampling)
-- **Resolution**: 1024×1024 pixels at 1.2cm ground resolution
-- **Total Size**: ~6.4GB of image data plus embeddings
-- **Quality Control**: Tiles with transient objects, such as vehicles, were excluded from the dataset. RGB imagery and canopy rasters are removed together to keep modalities aligned.
-## Use Cases
-This dataset is intended for:
-- Developing vision encoders robust to lighting variations
-- Semantic stability research in computer vision
-- Time-invariant feature learning
-- Remote sensing applications requiring lighting robustness
-- Comparative analysis of illumination effects on vision model features
-## Citation
-If you use this dataset in your research, please cite:
-```bibtex
-@dataset{mpg_ranch_light_stable_semantics_2024,
-  title={Light Stable Semantics Dataset},
-  author={Kyle Doherty and Erik Samose and Max Gurinas and Brandon Trabucco and Ruslan Salakhutdinov},
-  year={2024},
-  month={November},
-  url={https://huggingface.co/datasets/mpg-ranch/light-stable-semantics},
-  publisher={Hugging Face},
-  note={Aerial orthomosaic tiles with DINOv2 and DINOv3 embeddings for light-stable semantic vision encoder training},
-  location={MPG Ranch, Montana, USA},
-  survey_date={2024-11-07},
-  organization={MPG Ranch}
-}
-```
-## License
-This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.
-**Attribution Requirements:**
-- You must give appropriate credit to MPG Ranch
-- Provide a link to the license
-- Indicate if changes were made to the dataset