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README.md

@@ -1,23 +1,83 @@
 ---
-dataset_info:
-  features:
-  - name: image_t0
-    dtype: image
-  - name: image_t1
-    dtype: image
-  - name: image_t2
-    dtype: image
-  - name: idx
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 6673510618.0
-    num_examples: 620
-  download_size: 6673860222
-  dataset_size: 6673510618.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: cc-by-4.0
+task_categories:
+- computer-vision
+- feature-extraction
+tags:
+- remote-sensing
+- aerial-imagery
+- orthomosaic
+- lighting-invariance
+- semantic-stability
+- vision-encoder
+- time-series
+size_categories:
+- 1K<n<10K
+language:
+- en
+pretty_name: Light Stable Semantics
 ---
+
+# 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) to develop vision encoders that are semantically stable under varying lighting conditions. The dataset is designed for training computer vision models that can maintain consistent feature representations despite changes in illumination.
+
+### Dataset Summary
+
+- **Purpose**: Training light-stable semantic vision encoders
+- **Data Type**: Aerial orthomosaic tiles (RGBA, 1024x1024 pixels)
+- **Time Points**: 3 captures per location (morning, noon, afternoon)
+- **Coverage**: Lower Partridge area aerial survey
+- **Date**: November 7, 2024 (241107)
+- **Location**: MPG Ranch, Montana, USA
+
+### Data Structure
+
+Each record contains:
+- `image_t0`: Morning image (10:00, time=1000)
+- `image_t1`: Noon image (12:00, time=1200)  
+- `image_t2`: Afternoon image (15:00, time=1500)
+- `idx`: Tile identifier in format `{ROW}_{COL}`
+
+### Data Collection
+
+The orthomosaics were captured using drone surveys with identical geographic bounds but at different times of day to capture varying lighting conditions. All tiles:
+- Are 1024x1024 pixels of 1.2cm resolution
+- Maintain spatial alignment across time points
+- Use consistent geographic coordinates
+
+### Use Cases
+
+This dataset is intended for:
+- Training vision encoders robust to lighting changes
+- Semantic stability research in computer vision
+- Time-invariant feature learning
+- Remote sensing applications requiring lighting robustness
+
+## 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 captured at multiple times of day for light-stable semantic vision encoder training},
+  location={MPG Ranch, Montana, USA},
+  survey_date={2024-11-07},
+  organization={MPG Ranch}
+}
+```
+
+## Licensing
+
+This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license. 
+
+Under the following terms:
+- **Attribution** — You must give appropriate credit to MPG Ranch, provide a link to the license, and indicate if changes were made.