pypsa-meets-earth/earth-osm

# earth-osm One-command to extract infrastructure data from OpenStreetMap [![][badge-pypi-monthly-downloads]][pypi-url] [![][badge-codecov]][codecov-url] [![][badge-ci]][gh-repo-url] [![][badge-docs]][docs-url] [![][badge-pypi]][pypi-url] [![][badge-conda]][conda-url] [![][badge-release]][gh-release-url] [![][badge-license]][license] [![][badge-discord]][discord-url] [![][badge-gh-stars]][gh-stars-url] [![][badge-gh-forks]][gh-forks-url] [![][badge-gh-issues]][gh-issues-url] [![][badge-gh-pulls]][gh-pulls-url]

📚 Overview

earth-osm downloads, filters, cleans and exports infrastructure data from OpenStreetMap (OSM). It provides a Python API and a CLI interface to extract data for various infrastructure types, such as power lines, substations, and more.

🌟 Key Features

🔌 Extracts infrastructure data from OSM
🧹 Cleans and standardizes the data (coming soon)
🚀 No API rate limits (data served from GeoFabrik)
🐍 Provides a Python API
🖥️ Supports multiprocessing for faster extraction
📊 Outputs data in .csv and .geojson formats
🌍 Supports global data extraction
🖱️ Easy-to-use CLI interface

🚀 Getting Started

Installation

Install earth-osm using pip (recommended):

pip install earth-osm

Or with conda:

conda install --channel=conda-forge earth-osm

Basic Usage

Extract OSM data using the CLI:

earth_osm extract power --regions benin monaco --features substation line

This command extracts power infrastructure data for Benin and Monaco, focusing on substations and power lines. By default, the resulting .csv and .geojson files are stored in ./earth_data/out.

Load the extracted data using pandas:

import pandas as pd
import geopandas as gpd

# For Pandas
df_substations = pd.read_csv('./earth_data/out/BJ_raw_substations.csv')

# For GeoPandas
gdf_substations = gpd.read_file('./earth_data/out/BJ_raw_substations.geojson')

🛠️ CLI Reference

Extract Command

earth_osm extract <primary> --regions <region1> <region2> ... [options]

Arguments:

<primary>: Primary feature to extract (e.g power)

Required Options:

--regions: Specify one or more regions using ISO 3166-1 alpha-2, ISO 3166-2 codes, or full names

Tip: A list of regions is available at regions.md

Optional Arguments:

Argument	Description	Default
`--features`	Specify sub-features of the primary feature	All features
`--update`	Update existing data	False
`--no_mp`	Disable multiprocessing	False (MP enabled)
`--data_dir`	Path to data directory	'./earth_data'
`--out_dir`	Path to output directory	Same as data_dir
`--out_format`	Export format(s): csv and/or geojson	['csv', 'geojson']
`--agg_feature`	Aggregate outputs by feature	False
`--agg_region`	Aggregate outputs by region	False

🐍 Python API

For more advanced usage, you can use the Python API:

import earth_osm as eo

eo.save_osm_data(
    primary_name='power',
    region_list=['benin', 'monaco'],
    feature_list=['substation', 'line'],
    update=False,
    mp=True,
    data_dir='./earth_data',
    out_format=['csv', 'geojson'],
    out_aggregate=False,
)

🛠️ Development

To contribute to earth-osm, follow these steps:

(Optional) Install a specific version of earth_osm:

pip install git+https://github.com/pypsa-meets-earth/earth-osm.git@<required-commit-hash>

(Optional) Create a virtual environment for Python >=3.10:
```
python3 -m venv .venv
source .venv/bin/activate
```

Install the development dependencies:

pip install git+https://github.com/pypsa-meets-earth/earth-osm.git
pip install -r requirements-test.txt

Read the CONTRIBUTING.md file for more detailed information on how to contribute to the project.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

🤝 Community

Join our Discord community to connect with other users and contributors, ask questions, and get support.

📚 Documentation

For more detailed information, check out our full documentation.

Made with ❤️ by the PyPSA meets Earth team