---
license: mit
task_categories:
  - object-detection
tags:
  - disability-parking
  - accessibility
  - streetscape
dataset_info:
  features:
    - name: image
      dtype: image
    - name: width
      dtype: int32
    - name: height
      dtype: int32
    - name: objects
      sequence:
        - name: bbox
          sequence: float32
          length: 4
        - name: category
          dtype: int64
        - name: area
          dtype: float32
        - name: iscrowd
          dtype: bool
        - name: id
          dtype: int64
        - name: segmentation
          sequence:
            sequence: float32
  splits:
    - name: train
      num_examples: 3688
    - name: test
      num_examples: 717
    - name: valid
      num_examples: 720
---

# AccessParkCV

<strong>AccessParkCV</strong> is a deep learning pipeline that detects and characterizes the width of disability parking spaces from orthorectified aerial imagery. We publish a dataset of 7,069 labeled parking spaces (and 4,693 labeled access aisles), which we used to train the models making AccessParkCV possible.


## Dataset Description

This is an object detection dataset with 8 classes:

- objects
- access_aisle
- curbside
- dp_no_aisle
- dp_one_aisle
- dp_two_aisle
- one_aisle
- two_aisle

## Dataset Structure

### Data Fields

- `image`: PIL Image object
- `width`: Image width in pixels
- `height`: Image height in pixels  
- `objects`: Dictionary containing:
  - `bbox`: List of bounding boxes in [x_min, y_min, x_max, y_max] format
  - `category`: List of category IDs
  - `area`: List of bounding box areas
  - `iscrowd`: List of crowd flags (boolean)
  - `id`: List of annotation IDs
  - `segmentation`: List of polygon segmentations (each as list of [x1,y1,x2,y2,...] coordinates)

### Data Splits

| Split | Examples |
|-------|----------|
| train | 3688 |
| test | 717 |
| valid | 720 |

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("your-username/AccessParkCV")

# Access training data
train_dataset = dataset["train"]

# Example of accessing an item
item = train_dataset[0]
image = item["image"]
bboxes = item["objects"]["bbox"]
categories = item["objects"]["category"]
segmentations = item["objects"]["segmentation"]  # Polygon coordinates
```

## Citation

```bibtex
@inproceedings{hwang_wherecanIpark,
  title={Where Can I Park? Understanding Human Perspectives and Scalably Detecting Disability Parking from Aerial Imagery},
  author={Hwang, Jared and Li, Chu and Kang, Hanbyul and Hosseini, Maryam and Froehlich, Jon E.},
  booktitle={The 27th International ACM SIGACCESS Conference on Computers and Accessibility},
  series={ASSETS '25},
  pages={20 pages},
  year={2025},
  month={October},
  address={Denver, CO, USA},
  publisher={ACM},
  location={New York, NY, USA},
  doi={10.1145/3663547.3746377},
  url={https://doi.org/10.1145/3663547.3746377}
}
```