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---
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: validation
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.
(This repo contains the data in a HuggingFace format. For raw COCO format, see [link](https://huggingface.co/datasets/makeabilitylab/AccessParkCV_coco)).
## 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)
### Category IDs to Category
| Category ID | Class |
|-----------------|-----------------|
| 0 | objects |
| 1 | access_aisle |
| 2 | curbside |
| 3 | dp\_no\_aisle |
| 4 | dp\_one\_aisle |
| 5 | dp\_two\_aisle |
| 6 | one\_aisle |
| 7 | two\_aisle |
### Data Sources
| Region | Lat/Long Bounding Coordinates | Source Resolution | # images in dataset |
|-----------------|---------------------------------------------|-------------------|---------------------|
| Seattle | (47.9572, -122.4489), (47.4091, -122.1551) | 3 inch/pixel | 2,790 |
| Washington D.C. | (38.9979, -77.1179), (38.7962, -76.9008) | 3 inch/pixel | 1,801 |
| Spring Hill | (35.7943, -87.0034), (35.6489, -86.8447) | Unknown | 534 |
| Total | | | 5,125 |
### Class Composition
| Class | Quantity in dataset |
|----------------|---------------------|
| access\_aisle | 4,693 |
| curbside | 36 |
| dp\_no\_aisle | 300 |
| dp\_one\_aisle | 2,790 |
| dp\_two\_aisle | 402 |
| one\_aisle | 3,424 |
| two\_aisle | 117 |
| Total | 11,762 |
###
### Data Splits
| Split | Examples |
|-------|----------|
| train | 3688 |
| test | 717 |
| valid | 720 |
### Class splits
## Usage
```python
from datasets import load_dataset
train_dataset = load_dataset("makeabilitylab/disabilityparking", split="train", streaming=True)
example = next(iter(train_dataset))
# Example of accessing an item
image = example["image"]
bboxes = example["objects"]["bbox"]
categories = example["objects"]["category"]
segmentations = example["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}
}
``` |