Voxel51/parkseg12k_train

Dataset Card for ParkSeg12k: Parking Lot Segmentation Dataset

This is a FiftyOne dataset with 11,355 samples from the ParkSeg12k dataset, enhanced with NDVI calculations for parking lot segmentation.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/parkseg12k_train")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Sources

• Original Dataset: https://huggingface.co/datasets/UTEL-UIUC/parkseg12k
• Paper: A Pipeline and NIR-Enhanced Dataset for Parking Lot Segmentation
• GitHub Repository: https://github.com/UTEL-UIUC/ParkSeg12k

Uses

Direct Use

• Training semantic segmentation models for parking lot detection
• Urban planning and policy analysis
• Analyzing land use patterns in US cities
• Supporting parking reform policy discussions

Out-of-Scope Use

• This dataset is specific to US cities and may not generalize to other countries
• Not suitable for real-time parking occupancy detection (detects lot boundaries, not individual spaces)

Dataset Structure

FiftyOne Fields

Each sample contains:

• filepath: Path to RGB image (512x512 pixels, 30 cm/pixel resolution)
• segmentation: Binary segmentation mask for parking lots (0=background, 1=parking)
• nir: Near-infrared channel as heatmap (upsampled from NAIP imagery)
• ndvi: Normalized Difference Vegetation Index heatmap (range: -1 to 1)
• ndvi_mean: Mean NDVI value for the image
• ndvi_std: Standard deviation of NDVI values
• ndvi_min: Minimum NDVI value
• ndvi_max: Maximum NDVI value

NDVI Calculation

NDVI was computed using: (NIR - Red) / (NIR + Red)

• Values near 1: Dense vegetation
• Values near 0: Bare soil/pavement
• Negative values: Water bodies

This helps identify parking lot boundaries since many are surrounded by grass/vegetation.

Dataset Creation

Curation Rationale

Created to automate parking lot detection for urban planning discussions around minimum parking requirements (MPRs) and land use policy.

Source Data

Data Collection and Processing

• RGB imagery: Google Maps satellite tiles (30 cm/pixel)
• NIR imagery: National Agriculture Imagery Program (NAIP) - upsampled from 1m/pixel to 30cm/pixel
• Covers 45 US cities with ~35,000 annotated parking lots
• Total area: 297.7 km² with 62.5 km² of labeled parking

Who are the source data producers?

• Google Maps (RGB imagery)
• NAIP/USDA (NIR imagery)
• Parking Reform Network (initial annotations for 42 cities)
• OpenStreetMap (additional annotations for 3 cities)

Annotations

Annotation process

Manual refinement of initial annotations in QGIS, ensuring boundaries align with pavement edges rather than property lines.

Who are the annotators?

Students from the Urban Traffic & Economics Lab at UIUC, supervised by the paper authors.

Personal and Sensitive Information

Satellite imagery may incidentally capture vehicles and structures but no personally identifiable information is included.

Bias, Risks, and Limitations

• Dataset focuses on US cities; parking lot designs may differ internationally
• NIR channel contains tiling/mosaicking artifacts from orthorectification
• Temporal misalignment possible between RGB and NIR sources
• Urban-focused; may not generalize well to rural areas

Recommendations

• Be aware of NIR artifacts when training models
• Consider using NDVI statistics to filter samples by vegetation content
• Post-processing steps (edge simplification, building/road removal) recommended for deployment

Citation

BibTeX:

@article{qiam2024,
  title={A Pipeline and NIR-Enhanced Dataset for Parking Lot Segmentation},
  author={Shirin Qiam and Saipraneeth Devunuri and Lewis J. Lehe},
  journal={arXiv preprint arXiv:2412.13179},
  year={2024},
  url={https://arxiv.org/pdf/2412.13179}
}

APA: Qiam, S., Devunuri, S., & Lehe, L. J. (2024). A Pipeline and NIR-Enhanced Dataset for Parking Lot Segmentation. arXiv preprint arXiv:2412.13179.

Dataset Card Contact

For questions about the original dataset: {sqiam2, sd37, lehe}@illinois.edu