Datasets:
Modalities:
Geospatial
Languages:
English
Size:
1M<n<10M
Tags:
street view imagery
open data
data fusion
urban analytics
GeoAI
volunteered geographic information
License:
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) | |