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Datasets:
rsdmu
/
streetreview

Tasks:
Zero-Shot Classification
Image Classification
Image Segmentation
Modalities:
Image
Languages:
English
Size:
10K<n<100K
Tags:
urban-planning
montreal
publicspace
inclusivity
accessibility
participatory
License:
Dataset card Viewer Files Community
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ArrowInvalid
Message:      Float value 2.1 was truncated converting to int64
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2245, in cast_table_to_schema
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2246, in <listcomp>
                  cast_array_to_feature(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2102, in cast_array_to_feature
                  return array_cast(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1797, in wrapper
                  return func(array, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1949, in array_cast
                  return array.cast(pa_type)
                File "pyarrow/array.pxi", line 996, in pyarrow.lib.Array.cast
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/compute.py", line 404, in cast
                  return call_function("cast", [arr], options, memory_pool)
                File "pyarrow/_compute.pyx", line 590, in pyarrow._compute.call_function
                File "pyarrow/_compute.pyx", line 385, in pyarrow._compute.Function.call
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Float value 2.1 was truncated converting to int64
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1417, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1049, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1897, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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pointid
string
lgbtqia2+_practicality
float64
lgbtqia2+_inclusivity
float64
lgbtqia2+_aesthetics
float64
lgbtqia2+_accessibility
float64
handicapped_practicality
float64
handicapped_inclusivity
float64
handicapped_aesthetics
float64
handicapped_accessibility
float64
elderly_female_practicality
float64
elderly_female_inclusivity
float64
elderly_female_aesthetics
float64
elderly_female_accessibility
float64
elderly_male_practicality
int64
elderly_male_inclusivity
int64
elderly_male_aesthetics
int64
elderly_male_accessibility
int64
young_male_practicality
int64
young_male_inclusivity
int64
young_male_aesthetics
int64
young_male_accessibility
int64
young_female_practicality
float64
young_female_inclusivity
float64
young_female_aesthetics
float64
young_female_accessibility
float64
group_practicality
float64
group_inclusivity
float64
group_aesthetics
float64
group_accessibility
float64
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4
4
3
3
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i57
2
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2
2
2
1
3
2
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1
1
2
2
2
2
2
2
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2
3
2
2
i58
4
3.5
2
3.5
1
1
1
1
3.7
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4
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3.5
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3.2
2.6
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2.3
0.2
3.5
1.5
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i59
2.5
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1
1
1
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1
2.3
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1
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1
i60
3
1
1.5
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4
2
1
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2.6

StreetReview Dataset

image/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.

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:

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

Example Code 

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).

Citing StreetReview 

@dataset{streetreview2024,
  title = {StreetReview Dataset: Evaluating Urban Streetscapes for Inclusivity and Accessibility},
  author = {Rashid Mushkani},
  year = {2024},
  publisher = {Hugging Face},
  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:

© 2024 RSDMU. All rights reserved.

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