Use OSM API to download buildings and turn them into GeoPandas geodataframes

gistfile1.txt

import geopandas as gpd
import numpy as np
import pandas as pd
import requests

save_directory = '../data/'

osm_api_url_base = 'http://overpass.openstreetmap.ru/cgi/xapi_meta?'

regions = {
    'washingtondc_test' : (-77.0,38.99,-76.991,39.00),
    'moscow_test' : (37.40,55.90,37.405,55.905)
}

gdfs = {}

tag_query = 'way[building=*]'

for name, bbox in regions.items():
    osm_url = osm_api_url_base + tag_query + '[bbox={0},{1},{2},{3}]'.format(*bbox)

    retrieved = requests.get(osm_url)
    as_text = retrieved.text
    
    osm_filename = 'osm_{0}.osm'.format(name)
    with open(save_directory + osm_filename,'w') as f:
        f.write(as_text)
        
    gpkg_filename = 'osm_{0}.gpkg'.format(name)
    
    ! cd $save_directory; ogr2ogr $gpkg_filename $osm_filename
    
    gdfs[name] = gpd.read_file(save_directory + gpkg_filename, layer='multipolygons')

This gist is for use in a Jupyter notebook and uses the OpenStreetMap database and one of its API mirrors to download all records corresponding to buildings within a given bounding box. It then uses ogr2ogr to convert them from .osm to .gpkg files and load them in GeoPandas.