metadata

license: cc-by-sa-4.0
task_categories:
  - image-classification
  - image-segmentation
  - image-feature-extraction
language:
  - en
tags:
  - street view imagery
  - open data
  - data fusion
  - urban analytics
  - GeoAI
  - volunteered geographic information
  - machine learning
  - spatial data infrastructure
  - geospatial
size_categories:
  - 1M<n<10M

Global Streetscapes

Repository for the tabular portion of the Global Streetscapes dataset by the Urban Analytics Lab (UAL) at the National University of Singapore (NUS).

Content Breakdown

Global Streetscapes (62+ GB)
├── data/ (37 GB)
│   ├── 21 CSV files with 346 unique features in total and 10M rows each
├── manual_labels/ (23 GB)
│   ├── train/
│   │   ├── 8 CSV files with manual labels for contextual attributes (training)
│   ├── test/
│   │   ├── 8 CSV files with manual labels for contextual attributes (testing)
│   ├── img/
│       ├── 7 tar.gz files containing images for training and testing
├── models/ (2.8 GB)
│   ├── Trained models in checkpoint format
├── cities688.csv
│   ├── Basic information for the 688 cities including population, continent, and image count
├── info.csv
    ├── Overview of CSV files in `/data/` with description of each feature

Download Instructions

Please follow this guide from Hugging Face for download instructions. Please avoid using 'git clone' to download the repo as Git stores the files twice and will double the disk space usage to 124+ GB.

We have also provided a script download_folder.py to download a specifc folder from this dataset, instead of just a single file or the entire dataset.

To download the imagery portion (10 million images, ~6TB), please follow the code and documentation in our GitHub repo. Our Wiki contains instructions and a demo on how to filter the dataset for a subset of data of your interest and download the image files for them.

Contribution Guide

We welcome contributions to this dataset! Please follow these steps:

Propose changes:
- Open a discussion in the repository to describe your proposed changes or additions.
- We will revert with specifics on how we would like your contributions to be incorporated (e.g. which folder to add your files), to maintain a neat organisation.
File naming:
- Use meaningful and descriptive file names.
Submit changes:
- Fork the repository, implement your changes, and submit a pull request (PR). In your PR, include an informative description of your changes (e.g. explaining their structure, features, and purpose) and how you would like to be credited.

Upon merging your PR, we will update the Changelog and Content Breakdown on this Dataset Card accordingly to reflect the changes and contributors.

For any questions, please contact us via Discussions.

Changelog

YYYY-MM-DD

Read more about this project on its website, which includes an overview of this effort together with the background, paper, examples, and FAQ.

A free version (postprint / author-accepted manuscript) can be downloaded here.

Citation

To cite this work, please refer to the paper:

Hou Y, Quintana M, Khomiakov M, Yap W, Ouyang J, Ito K, Wang Z, Zhao T, Biljecki F (2024): Global Streetscapes — A comprehensive dataset of 10 million street-level images across 688 cities for urban science and analytics. ISPRS Journal of Photogrammetry and Remote Sensing 215: 216-238. doi:10.1016/j.isprsjprs.2024.06.023

BibTeX:

@article{2024_global_streetscapes,
 author = {Hou, Yujun and Quintana, Matias and Khomiakov, Maxim and Yap, Winston and Ouyang, Jiani and Ito, Koichi and Wang, Zeyu and Zhao, Tianhong and Biljecki, Filip},
 doi = {10.1016/j.isprsjprs.2024.06.023},
 journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
 pages = {216-238},
 title = {Global Streetscapes -- A comprehensive dataset of 10 million street-level images across 688 cities for urban science and analytics},
 volume = {215},
 year = {2024}
}

Datasets:

NUS-UAL
/

global-streetscapes