How to Convert a .OSM File to a .GeoJSON File using Python

3 min readOct 11, 2024

If you’re working with geospatial data, you’ll likely come across OpenStreetMap (.osm) files and may need to convert them to a more widely-used format like GeoJSON. Whether you’re analyzing urban infrastructure, building a map visualization, or working on a geospatial project, GeoJSON is the go-to format for web mapping and data interchange. A few websites do the job for you, but they are either paid or have a conversion limit. In this article, I’ll walk you through the process of converting .osm files to .geojson using Python, completely free of charge.

To get started, you’ll need Python installed on your machine along with two libraries: osmnx and geopandas.

Installing Required Libraries

First, install the necessary Python packages by running:

pip install osmnx geopandas

osmnx is a Python library that simplifies downloading and working with OpenStreetMap data.
geopandas extends the capabilities of pandas to allow for spatial data manipulation.

Python Code to Extract Data from .OSM and Save as .GeoJSON

Let’s assume you have an .osm file containing data from OpenStreetMap. You can use the following code to extract specific features like roads, buildings, and land use, and save them as separate .geojson files.

import osmnx as ox
import geopandas as gpd

# Load the .osm file into a GeoDataFrame
gdf = ox.geometries_from_xml('your_osm_file.osm')

# Extract roads and save to .geojson
roads = gdf[gdf['highway'].notnull()]
roads.to_file('output_directory/roads.geojson', driver='GeoJSON')

# Extract buildings and save to .geojson
buildings = gdf[gdf['building'].notnull()]
buildings.to_file('output_directory/buildings.geojson', driver='GeoJSON')

# Extract land use and save to .geojson
landuse = gdf[gdf['landuse'].notnull()]
landuse.to_file('output_directory/landuse.geojson', driver='GeoJSON')

Breakdown of the Code

Load the OSM data: The osmnx.geometries_from_xml function loads the OpenStreetMap data into a GeoDataFrame. You simply need to provide the path to your .osm file.
Extract specific features: By filtering the GeoDataFrame, you can extract roads, buildings, and land use. The filtering uses the .notnull() function to select only rows that contain non-empty values for specific features (like highway for roads or building for buildings).
Save to .GeoJSON: The to_file function saves the filtered data into GeoJSON format, specifying the output directory and setting the driver parameter to 'GeoJSON'.

Why This Method?

This method is cost-free and highly efficient for extracting data. Libraries like osmnx and geopandas simplify the workflow, allowing you to handle large and complex .osm files without having to rely on external paid services. You also get full control over what data you extract and how you use it in your projects.

Conclusion

Converting .osm files to .geojson is a crucial step in making OpenStreetMap data more usable for visualization and analysis. With just a few lines of Python code and open-source tools, you can easily extract and manipulate geospatial data for your next project.

If you’re working on geospatial analysis or web mapping, this process will help you bridge the gap between complex OSM data and the lightweight, widely-used GeoJSON format. Happy mapping!