Update dataset card metadata - 2025-09-22T23:48:11.339847

README.md

@@ -1,136 +1,10 @@
 ---
-dataset_info:
-- config_name: default
-  features:
-  - name: idx
-    dtype: string
-  - name: image_t0
-    dtype: image
-  - name: image_t1
-    dtype: image
-  - name: image_t2
-    dtype: image
-  - name: canopy_height
-    dtype:
-      array2_d:
-        shape:
-        - 1024
-        - 1024
-        dtype: int32
-  splits:
-  - name: train
-    num_bytes: 4905235380
-    num_examples: 487
-  - name: test
-    num_bytes: 1221459061
-    num_examples: 122
-  download_size: 3688072446
-  dataset_size: 6126694441
-- config_name: dinov2_base
-  features:
-  - name: idx
-    dtype: string
-  - name: cls_t0
-    list: float32
-    length: 768
-  - name: cls_t1
-    list: float32
-    length: 768
-  - name: cls_t2
-    list: float32
-    length: 768
-  - name: patch_t0
-    dtype:
-      array2_d:
-        shape:
-        - 256
-        - 768
-        dtype: float32
-  - name: patch_t1
-    dtype:
-      array2_d:
-        shape:
-        - 256
-        - 768
-        dtype: float32
-  - name: patch_t2
-    dtype:
-      array2_d:
-        shape:
-        - 256
-        - 768
-        dtype: float32
-  splits:
-  - name: train
-    num_bytes: 1154971327
-    num_examples: 487
-  - name: test
-    num_bytes: 289335733
-    num_examples: 122
-  download_size: 1487171455
-  dataset_size: 1444307060
-- config_name: dinov3_sat
-  features:
-  - name: idx
-    dtype: string
-  - name: cls_t0
-    list: float32
-    length: 1024
-  - name: cls_t1
-    list: float32
-    length: 1024
-  - name: cls_t2
-    list: float32
-    length: 1024
-  - name: patch_t0
-    dtype:
-      array2_d:
-        shape:
-        - 196
-        - 1024
-        dtype: float32
-  - name: patch_t1
-    dtype:
-      array2_d:
-        shape:
-        - 196
-        - 1024
-        dtype: float32
-  - name: patch_t2
-    dtype:
-      array2_d:
-        shape:
-        - 196
-        - 1024
-        dtype: float32
-  splits:
-  - name: train
-    num_bytes: 1180053775
-    num_examples: 487
-  - name: test
-    num_bytes: 295619221
-    num_examples: 122
-  download_size: 1520934285
-  dataset_size: 1475672996
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
-- config_name: dinov2_base
-  data_files:
-  - split: train
-    path: dinov2_base/train-*
-  - split: test
-    path: dinov2_base/test-*
-- config_name: dinov3_sat
-  data_files:
-  - split: train
-    path: dinov3_sat/train-*
-  - split: test
-    path: dinov3_sat/test-*
+license: cc-by-4.0
+task_categories:
+- feature-extraction
+- image-to-image
+language:
+- en
 tags:
 - remote-sensing
 - aerial-imagery
@@ -144,12 +18,160 @@ tags:
 - embeddings
 - multi-config
 pretty_name: Light Stable Semantics
-license: cc-by-4.0
-language:
-- en
-task_categories:
-- feature-extraction
-- image-to-image
 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
+