Duplicate from NUS-UAL/global-streetscapes

Co-authored-by: Matias Quintana <matiasqr@users.noreply.huggingface.co>

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README.md

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+---
+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](https://ual.sg/project/global-streetscapes/) by the [Urban Analytics Lab (UAL)](https://ual.sg/) at the National University of Singapore (NUS).
+
+## Content Breakdown
+```
+Global Streetscapes (74 GB)
+├── data/ (49 GB)
+│   ├── 21 CSV files with 346 unique features in total and 10M rows each (37 GB)
+│   ├── parquet/ (12 GB) (New)
+│       ├── 21 Parquet equivalents of the 21 CSV files (New)    
+│       ├── 1 combined Parquet file (New)
+├── 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](https://huggingface.co/docs/huggingface_hub/guides/download) 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.
+
+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](https://github.com/ualsg/global-streetscapes).
+Our [Wiki](https://github.com/ualsg/global-streetscapes/wiki/2-Download-images) 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
+
+Read more about this project on [its website](https://ual.sg/project/global-streetscapes/), which includes an overview of this effort together with the background, [paper](https://doi.org/10.1016/j.isprsjprs.2024.06.023), examples, and FAQ.
+
+A free version (postprint / author-accepted manuscript) can be downloaded [here](https://ual.sg/publication/2024-global-streetscapes/).
+
+## Citation
+
+To cite this work, please refer to the [paper](https://doi.org/10.1016/j.isprsjprs.2024.06.023):
+
+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](https://doi.org/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}
+}
+```
+
+## Contribution Guide
+We welcome contributions to this dataset! Please follow these steps:
+
+1. **Propose changes**:
+   - Open a [discussion](https://huggingface.co/datasets/NUS-UAL/global-streetscapes/discussions) 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.
+
+2. **File naming**:
+   - Use meaningful and descriptive file names.
+
+3. **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](https://huggingface.co/datasets/NUS-UAL/global-streetscapes/discussions).
+
+## Changelog
+
+**2025-02-19**
+
+**Added:**
+
+- New [data/parquet/](https://huggingface.co/datasets/NUS-UAL/global-streetscapes/tree/main/data/parquet) folder containing Parquet files for improved efficiency in large-scale data processing.
+  - 21 Parquet equivalents of the 21 CSV files
+  - 1 Parquet combining contextual.csv, metadata_common_attributes.csv, segmentation.csv, simplemaps.csv, ghsl.csv, perception.csv, places365.csv, and osm.csv
+
+**Change contributed by:**
+
+Donnelly, C., Hadjiivanov, A., & Kalverla, P. (2024). streetscapes. Zenodo. [https://doi.org/10.5281/zenodo.14283533](https://doi.org/10.5281/zenodo.14283533)
+
+**Refs:**
+
+[PR #6](https://huggingface.co/datasets/NUS-UAL/global-streetscapes/discussions/6) [Discussion #5](https://huggingface.co/datasets/NUS-UAL/global-streetscapes/discussions/5)

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+version https://git-lfs.github.com/spec/v1
+oid sha256:11f05190d9e76f6b5ace876553eb7f6fa73972e5412c99d9ee21533405a6acec
+size 4323196276

data/simplemaps.csv

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+oid sha256:1673f3c70a6c2549fb065c0908d4bb0fab9cdd5ea095341a668a5cfaa9d6003e
+size 1597901155

data/speed.csv

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+oid sha256:f7458dc0524a0fc8d9763478147dc3649a349d8d9b7a00030608f68abaff161f
+size 2816045821

download_folder.py

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+from huggingface_hub import HfApi, hf_hub_download
+
+
+def download_folder(repo_id, repo_type, folder_path, local_dir):
+    """
+    Download an entire folder from a huggingface dataset repository.
+
+    repo_id : string
+        The ID of the repository (e.g., 'username/repo_name').
+    repo_type : string
+        Type of the repo, dataset or model.
+    folder_path : string
+        The path to the folder within the repository.
+    local_dir : string
+        Local folder to download the data. This mimics git behaviour
+    """
+    api = HfApi()
+    # list all files in the repo, keep the ones within folder_path
+    all_files = api.list_repo_files(repo_id, repo_type=repo_type)
+    files_list = [f for f in all_files if f.startswith(folder_path)]
+
+    # download each of those files
+    for file_path in files_list:
+        hf_hub_download(repo_id=repo_id, repo_type=repo_type,
+                        filename=file_path, local_dir=local_dir)
+
+
+# Download entire data/ folder
+repo_id = "NUS-UAL/global-streetscapes" # you can replace this for other huggingface repos
+repo_type = "dataset" # required by the API when the repo is a dataset
+folder_path = "data/" # replace the folder you want within the repo 
+local_dir = "global-streetscapes/" # the local folder in your computer where it will be downloaded
+
+# By default, huggingface download them to the .cache/huggingface folder
+download_folder(repo_id, repo_type, folder_path, local_dir)
+
+# Download 2 additional files
+hf_hub_download(repo_id=repo_id, repo_type=repo_type,
+                filename="cities688.csv", local_dir=local_dir)
+hf_hub_download(repo_id=repo_id, repo_type=repo_type,
+                filename="info.csv", local_dir=local_dir)

info.csv

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+oid sha256:ad4fb01eb6ecba9cd36bbc5c05549e428835f7d914ef6d4d457a4ac3faed8743
+size 37101

manual_labels/img/1.tar.gz

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+size 3982073892

manual_labels/img/2.tar.gz

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