metadata

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: 3569866407
      num_examples: 609
  download_size: 3570065377
  dataset_size: 3569866407
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
task_categories:
  - 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:

@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) 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.