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