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:

data/ (37 GB)
- 21 csv files with 346 unique features in total and 10 million rows each to characterise the 10 million street-level images in our dataset
manual_labels/ (23 GB)
- train/
  - 8 csv files of manual labels for training computer vision models to classify 8 different contextual characteristics of a street view image, along with other metadata such as the image's location, city, file path etc.
- test/
  - 8 csv files of manual labels for model testing, along with other metadata such as the image's location, city, file path etc.
- img/
  - 7 tar.gz files containing all images used for training and testing
models/ (2.8 GB)
- 8 ckpt files storing the trained models
cities688.csv contains basic information for the 688 cities included in the dataset, such as population, continent, image count etc.
info.csv overviews the content of each csv file in /data and explains the 346 features

This repository has a total size of about 62 GB.

Please follow this guide from huggingface 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 one 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.

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

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}
}

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