Leveraging OpenStreetMap (OSM) Data for Machine Learning with MATLAB

OpenStreetMap (OSM) is a collaborative project to create a free and open map of the world. It contains a wealth of information about roads, buildings, and other features that can be used for a variety of applications. However, processing OSM data can be challenging, especially for machine learning applications that require large datasets. That’s where our new OSM tool in MATLAB comes in handy!

Our OSM tool provides a set of functions to parse OSM XML data files, extract nodes and ways data, classify ways into categories, build geo shapes from ways, and filter ways by category. These functions allow you to quickly and easily analyze OSM data and create visualizations of buildings and roads on maps.

Here’s a brief explanation of each function:

• osm_to_struct: loads an OSM XML data file and returns a MATLAB data structure of the parsed file.
• get_nodes: extracts the nodes data from the struct created by osm_to_struct and returns a MATLAB data table of the nodes in the OpenStreetMap file containing the following columns: id, lat, and lon.
• get_ways: extracts the ways data from the struct created by osm_to_struct and returns a MATLAB data table of the ways in the OpenStreetMap file containing the following columns: id, timestamp, node_ids, and tags.
• get_bounds: extracts the bounding box from the struct created by osm_to_struct and returns a MATLAB polyshape structure that defines the boundary of the map.
• clean_ways: removes ways that do not have associated tags from the ways data table returned by get_ways.
• classify_ways: classifies the ways by adding primary and secondary categories such as building or highway.
• build_geo_shapes: builds the geographical shapes from the ways table based on the primary category.
• filter_by_category: filters the ways table based on the primary category.

But how can this tool be used in machine learning? The answer is simple: by obtaining datasets from OSM maps. For example, you can use our tool to extract data about buildings in a specific area and create a dataset for building classification. By classifying buildings as residential, commercial, or industrial, you can train a machine learning model to recognize these categories in new areas.

Similarly, you can use our tool to extract data about roads and create a dataset for road classification. By classifying roads as highways, residential streets, or commercial streets, you can train a machine learning model to recognize these categories in new areas.

Here’s an example of how to use our OSM tool to extract data about buildings in New York City:

% Load OSM file
osm_file = 'new-york.osm';
struc = osm_to_struct(osm_file);

% Extract ways and filter by building category
ways = get_ways(struc);
ways = clean_ways(ways);
classified_ways = classify_ways(ways);
buildings = filter_by_category(classified_ways, 'building');

% Get nodes and build geo shapes
nodes = get_nodes(struc);
shaped_buildings = build_geo_shapes(buildings, nodes);

% Save dataset to file
dataset = table();
for i = 1:size(shaped_buildings, 1)
    shape = shaped_buildings(i).geo_shape;
    category = shaped_buildings(i).secondary_category;
    dataset = [dataset; {shape, category}];
end
save('new-york-buildings.mat', 'dataset');

This code loads an OSM file of New York City and extracts data about buildings. It then filters the data to include only buildings and builds a geo shape for each building. Finally, it saves the dataset to a file in MATLAB format. The resulting dataset can be used to train a machine learning model for building classification.

The possibilities are endless! Our OSM tool provides a streamlined way to obtain datasets from OSM maps and use them for machine learning applications. With a little creativity, you can use this tool to solve a wide range of problems related to maps and geography.

If you’re interested in using our OSM tool, you can check out the code on our GitHub repository. We hope that this tool will be useful for researchers and developers who work with OSM data and want to leverage it for machine learning applications.

Let us know what you think and feel free to share with others who might find this tool helpful!