README.md

---
datasets:
- rsdmu/streetreview
task_categories:
- zero-shot-classification
- image-classification
- image-segmentation
- image-feature-extraction
tags:
- urban-planning
- montreal
- publicspace
- inclusivity
- accessibility
- participatory
license: cc-by-4.0
language:
- en
size_categories:
- 10K<n<100K
pretty_name: Street Review Dataset
annotations_creators:
- crowdsourced
- expert-generated
---


# StreetReview Dataset

![image/png](https://cdn-uploads.huggingface.co/production/uploads/657d0fd583543a061b23b027/8rTxCdOovDoGAGjTYVSMw.png)

## Overview


**StreetReview** is a curated dataset designed to evaluate the inclusivity, accessibility, aesthetics, and practicality of urban streetscapes, particularly in a multicultural city context. Focused on Montréal, Canada, the dataset combines diverse demographic evaluations with rich metadata and street-view imagery. It aims to advance research in urban planning, public space design, and machine learning applications for creating inclusive and user-friendly urban environments.

## Table of Contents

- [Overview](#overview)
- [Dataset Structure](#dataset-structure)
  - [Root Directory](#root-directory)
  - [Street Image Data](#street-image-data)
  - [Street Evaluation Data](#street-evaluation-data)
- [Methodology](#methodology)
  - [Participatory Evaluation Process](#participatory-evaluation-process)
  - [Data Collection](#data-collection)
- [Data Fields](#data-fields)
  - [Metadata](#metadata)
  - [Evaluations](#evaluations)
- [Usage](#usage)
  - [Cloning the Repository](#cloning-the-repository)
  - [Example Code](#example-code)
- [License](#license)
- [Citing StreetReview](#citing-streetreview)
- [Contributing](#contributing)
- [Contact](#contact)

## Dataset Structure

The **StreetReview** dataset is organized as follows:

### Root Directory

- **`metadata.csv`**: Comprehensive metadata for each evaluation point.
- **`street_eval/`**: CSV files containing evaluation data for individual street sections.
- **`street_img/`**: Street-view images categorized by street and section.

### Street Image Data

Images are stored in `street_img/` and organized into folders by street and section, with three perspectives per section (`_main`, `_head`, `_tail`). Example structure:

```
street_img/
├── i01_cote_sainte_catherine_main/
│   ├── main_001.jpg
│   ├── main_002.jpg
│   ...
└── i02_rue_berri_main/
    ├── main_001.jpg
    ├── main_002.jpg
    ...
```

### Street Evaluation Data

Evaluation data is stored in `street_eval/` as CSV files named after their corresponding street section. Example:

```
street_eval/
├── i01_evaluations.csv
├── i02_evaluations.csv
...
```

## Methodology

### Participatory Evaluation Process

The dataset was created using a participatory approach to capture diverse urban experiences:

1. **Individual Evaluation**: Participants rated 20 street on four criteria using a color-coded system.
2. **Group Evaluation**: In focus groups, participants reassessed images collectively and refined their evaluations.

### Data Collection

- **Participants**: 28 individuals contributed to criteria development; 12 participated in detailed evaluations.
- **Evaluation Points**: 60 points across 20 streets, with two images per point.
- **Dataset Expansion**: Up to 250 images per point, rotated for diversity.

## Data Fields

### Metadata

The `metadata.csv` file contains attributes such as:

| Field                  | Description                          |
|------------------------|--------------------------------------|
| `point_id`             | Unique identifier                   |
| `sidewalk_width`       | Width of sidewalks                  |
| `greenery_presence`    | Presence of greenery                |
| `building_height`      | Height of adjacent buildings        |
| ...                    | ...                                 |

### Evaluations

Each CSV file in `street_eval/` includes ratings from various demographic groups. Ratings are based on a 1-4 scale. For example, a score of 1 for accessibility means "not accessible," scores of 2 or 3 indicate "average accessibility," and a score of 4 represents "highest accessibility."

| Field                     | Description                     |
|---------------------------|---------------------------------|
| `lgbtqia2+_accessibility` | Accessibility rating by LGBTQIA2+ |
| `elderly_male_practicality` | Practicality rating by elderly males |
| `group_inclusivity`       | Inclusivity rating by groups of 3-5 diverse individuals    |
| ...                       | ...                             |

## Usage

### Cloning the Repository

Clone the repository with:

```bash
git clone https://huggingface.co/datasets/rsdmu/streetreview
```

### Example Code

```python
import pandas as pd
from PIL import Image
import os

# Load metadata
metadata = pd.read_csv('metadata.csv')

# Load evaluation data
eval_data = pd.read_csv('street_eval/i01_evaluations.csv')

# Display an image
image_path = 'street_img/i01_cote_sainte_catherine_main/main_001.jpg'
image = Image.open(image_path)
image.show()
```

## License

Licensed under [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).

## Citing StreetReview

```bibtex
@dataset{streetreview2024,
  title = {StreetReview Dataset: Evaluating Urban Streetscapes for Inclusivity and Accessibility},
  author = {Rashid Mushkani},
  year = {2025},
  url = {https://huggingface.co/datasets/rsdmu/streetreview}
}
```

## Contributing

We welcome contributions! Please fork the repository, make changes, and submit a pull request.

## Contact

For inquiries, contact:

- **Email**: [Rashid Mushkani](mailto:[email protected])
- **Website**: [Rashid Mushkani](https://rsdmu.com)
- **GitHub**: [RSDMU](https://github.com/rsdmu)

---

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