Note

This is a Hugging Face dataset. Learn how to load datasets from the Hub in the Hugging Face integration docs.

Hugging Face

Dataset Card for ParkSeg12k: Parking Lot Segmentation Dataset#

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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 Description#

  • Curated by: UTEL-UIUC (Urban Traffic & Economics Lab, University of Illinois at Urbana-Champaign)

  • Enhanced by: Harpreet Sahota (FiftyOne conversion and NDVI calculations)

  • Language(s) (NLP): en

  • License: See original dataset for license information

Dataset Sources#

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