| --- |
| 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} |
| } |
| ``` |
