Python tool to extract large-amounts of OpenStreetMap data
Project description
earth-osm
earth-osm is a free software tool that can extract large-amounts of OpenStreetMap data. It implements filters and multi-processing for fast and memory-efficient computations. You can extract e.g. power lines for Africa on your laptop. It builds on esy-osmfilter and improves its package design, usability and performance.
Getting Started
Install earth-osm with pip:
pip install earth-osm
Or with conda:
conda install --channel=conda-forge earth-osm
Extract osm data
# Example CLI command
earth_osm extract power --regions benin monaco --features substation line
This will extract
primary feature = power for the regions = benin and monaco and the secondary features = substation and line.
By default the resulting .csv and .geojson are stored in ./earth_data/out
Load the substation data for benin using pandas
# 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')
Other Arguments
usage: earth_osm extract primary --regions region1, region2 --features feature1, feature2 --data_dir DATA_DIR [--update] [--mp]
primary (e.g power, water, road, etc) NOTE: currently only power is supported
--regions region1 region2 ... (use either iso3166-1:alpha2 or iso3166-2 codes or full names as given by running 'earth_osm view regions')
--features feature1 feature2 ... (optional, use sub-features of primary feature, e.g. substation, line, etc)
--update (optional, update existing data, default False)
--mp (optional, use multiprocessing, default True)
--data_dir (optional, path to data directory, default './earth_data')
--out_format (optional, export format options csv or geojson, default csv)
--out_aggregate (options, combine outputs per feature, default False)
Advanced Usage
import earth_osm as eo
eo.get_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
(Optional) Intstall 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
Read the CONTRIBUTING.md file.
pip install git+https://github.com/pypsa-meets-earth/earth-osm.git
pip install -r requirements-test.txt
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