README.md

---
license: cc-by-sa-4.0
dataset_info:
  features:
  - name: id
    dtype: string
  - name: crop_id
    dtype: string
  - name: question
    dtype: string
  - name: user_id
    dtype: string
  - name: answer
    dtype: string
  - name: cant_solve
    dtype: bool
  - name: created_at
    dtype: string
  - name: duration_ms
    dtype: int64
  - name: wp_id
    dtype: string
  splits:
  - name: ecp
    num_bytes: 233586835
    num_examples: 1035840
  - name: zod
    num_bytes: 37672526
    num_examples: 175962
  download_size: 93553083
  dataset_size: 271259361
configs:
- config_name: default
  data_files:
  - split: ecp
    path: data/ecp-*
  - split: zod
    path: data/zod-*
task_categories:
- question-answering
language:
- en
tags:
- crowdsourcing
pretty_name: Crowdsourced VRU Annotations
---

# Crowdsourced VRU Annotations

## Dataset Summary

This dataset provides tabular annotations from two underlying datasets — **ECP** and **ZOD** — and is organized into two splits (`ecp` and `zod`).  
It contains the results of crowdsourced annotation tasks focusing on **vulnerable road users (VRUs)**.

The underlying examples are *image crops* showing bounding boxes of VRUs from the ECP and ZOD datasets. Each crop is referenced via a `crop_id`. The actual image pixels are **not** included in this repository; instead, we provide per-crop annotations and auxiliary metadata that reference these crops.

---

## Dataset at a glance

- **Splits**: `ecp`, `zod`  
- **Domain**: Vulnerable road users (pedestrians, cyclists, persons on mobility devices, etc.)  
- **Data types**: tabular annotation rows (answers), auxiliary CSV metadata files (`ecp/boxes.csv`, `zod/attributes.csv`)

---

## Annotation tasks

For each crop, annotators answered one or more binary questions (with an additional `can't solve` option):

- **ECP tasks (6 questions)**  
  - `human being`: Is this a human being?  
  - `statue/mannequin`: Is this a statue or mannequin (i.e. not a real person)?  
  - `reflection of a person`: Is this a reflection (e.g. in glass)?  
  - `on wheels`: Is the person on wheels?  
  - `on poster/picture/billboard`: Is the person shown on a poster/picture/billboard?  
  - `on bike`: Is the person on a bike?

- **ZOD tasks (1 question)**  
  - `human being`: Is this a human being?

Answers are chosen from `yes`, `no`, or `can't solve`. When `can't solve` is chosen the `answer` field is `None` / `NaN` and the boolean `cant_solve` flag is set.

---

## Annotation design

- Annotations were collected from multiple human annotators. Each crop/question pair was answered multiple times (repeats).
  - **ECP**: 5–12 repeats per task; an adaptive allocation protocol increases the number of annotators for tasks with higher disagreement.
  - **ZOD**: fixed 11 repeats per task.
- Tasks were grouped and issued in **work packages**:
  - **ECP**: nominal size 20 tasks per work package, time limit 300 seconds.
  - **ZOD**: nominal size 30 tasks per work package, time limit 540 seconds.
- Due to concurrency and runtime behaviour, some submitted work packages may contain fewer tasks than the nominal size.
- Each submitted work package receives a unique `wp_id` and a submission timestamp — these are present in the data.

---

## Dataset statistics

- **ECP**
  - Tasks per crop: 6 (see list above)
  - Annotated crops: 32,711
  - Repeats per task: 5–12 (adaptive)
  - 1,035,840 individual answers

- **ZOD**
  - Tasks per crop: 1 (human being)
  - Annotated crops: 16,000
  - Repeats per task: 11
  - 175,962 individual answers

---

## Tabular data format

Both splits (`ecp` and `zod`) share the same annotation-row schema. Each row corresponds to one single annotator response to one task:

- `id` (`str`): globally unique identifier for the row  
- `crop_id` (`str`): identifier of the crop (the bounding-box image crop)  
- `question` (`str`): identifier of the question asked (e.g., `human being`)  
- `user_id` (`str`): identifier of the annotator (unique within the annotation system)  
- `answer` (`Optional[str]`): submitted answer; `None` / `NaN` if `cant_solve` is true  
- `cant_solve` (`bool`): whether the annotator marked the task as unsolvable  
- `created_at` (`datetime64[ns, UTC+01:00]`): ISO8601 timestamp when the work package was processed (note: this is the processing/submission time for the work package, not the individual annotation start time)  
- `duration_ms` (`int`): duration for the task in milliseconds  
- `wp_id` (`str`): work package identifier the task belongs to

---

## Metadata files

The repository also contains auxiliary metadata CSVs. These are provided as separate files so consumers can join/merge them with the annotation rows using `crop_id` if and when needed.

### `ecp/boxes.csv`

Schema:
- `crop_id` (`str`): unique id of the crop (reference key)  
- `image_path` (`str`): path of the original ECP image the crop belongs to  
- `left`, `top`, `right`, `bottom` (`int`): bounding box coordinates (upper-left and bottom-right corners)

### `zod/attributes.csv`

Schema:
- `crop_id` (`str`): unique id of the crop (reference key)  
- `label` (`str`): original ZOD label for the object  
- `attributes` (`json/object`): dictionary with object attributes (e.g., occlusion level) — unchanged from ZOD  
- `left`, `top`, `width`, `height` (`float`): bounding box coordinates (upper-left corner, width, height)

---

## Usage example

Load annotation tables (answers) via `datasets`:

```python
from datasets import load_dataset

REPO_ID = "cklugmann/crowdsourced-vru-annotations"
dataset = load_dataset(REPO_ID)

# Example: load ECP answers into a pandas DataFrame
df_answers_ecp = (
    dataset["ecp"]
    .to_pandas()
    .set_index("id")
)
```

Load ECP boxes metadata (without merging) using `hf_hub_download`:

```python
import pandas as pd
from huggingface_hub import hf_hub_download

df_boxes_ecp = (
    pd.read_csv(hf_hub_download(
        repo_id=REPO_ID,
        repo_type="dataset",
        filename="ecp/boxes.csv"
    ))
    .set_index("crop_id")
)
```

(Analogously you can load `zod/attributes.csv` with `hf_hub_download`.)

---

## How we recommend working with the data

- Use `load_dataset(...)` to fetch annotation rows as Hugging Face `Dataset` objects (one split per underlying source: `ecp`, `zod`).  
- Use `hf_hub_download(...)` to fetch auxiliary CSVs (boxes/attributes) as raw files and load them into Pandas with `pd.read_csv(...)`.  
- Perform any merging/joins yourself using `crop_id` so you keep the original annotation rows unchanged and control join semantics and filtering.

---

## License

This dataset is released under **CC-BY-SA-4.0**.

The dataset builds on:
- **ECP dataset** — [Link to website](https://eurocity-dataset.tudelft.nl/)
- **ZOD dataset** — [Link to website](https://zod.zenseact.com/)

---

## Citation

If you use this dataset in your research, please consider citing:

```bibtex
@misc{liao2025minorityreportsbalancingcost,
      title={Minority Reports: Balancing Cost and Quality in Ground Truth Data Annotation},
      author={Hsuan Wei Liao and Christopher Klugmann and Daniel Kondermann and Rafid Mahmood},
      year={2025},
      eprint={2504.09341},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2504.09341},
}
```