OSM PBF Loader¶

OSMPbfLoader can really quickly parse full OSM extract in the form of *.osm.pbf file.

It can download and parse a lot of features much faster than the OSMOnlineLoader, but it's much more useful when a lot of different features are required at once (like when using predefined filters).

When only a single or few features are needed, OSMOnlineLoader might be a better choice, since OSMPbfLoader will use a full extract of all features in a given region and will have to iterate over all of them.

Run this notebook in Google Colab:

Remember to install the srai library before running the notebook:

%pip install srai[all]

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import geopandas as gpd
from shapely.geometry import Point, box

from srai.constants import REGIONS_INDEX, WGS84_CRS
from srai.geometry import buffer_geometry
from srai.loaders.osm_loaders import OSMPbfLoader
from srai.loaders.osm_loaders.filters import GEOFABRIK_LAYERS, HEX2VEC_FILTER
from srai.loaders.osm_loaders.filters.popular import get_popular_tags
from srai.regionalizers import geocode_to_region_gdf
import geopandas as gpd
from shapely.geometry import Point, box

from srai.constants import REGIONS_INDEX, WGS84_CRS
from srai.geometry import buffer_geometry
from srai.loaders.osm_loaders import OSMPbfLoader
from srai.loaders.osm_loaders.filters import GEOFABRIK_LAYERS, HEX2VEC_FILTER
from srai.loaders.osm_loaders.filters.popular import get_popular_tags
from srai.regionalizers import geocode_to_region_gdf

Using OSMPbfLoader to download data for a specific area¶

Download all features from `HEX2VEC_FILTER` in Warsaw, Poland¶

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loader = OSMPbfLoader()
warsaw_gdf = geocode_to_region_gdf("Warsaw, Poland")
warsaw_features_gdf = loader.load(warsaw_gdf, HEX2VEC_FILTER)
warsaw_features_gdf
loader = OSMPbfLoader()
warsaw_gdf = geocode_to_region_gdf("Warsaw, Poland")
warsaw_features_gdf = loader.load(warsaw_gdf, HEX2VEC_FILTER)
warsaw_features_gdf

Finished operation in 0:00:55

Out[2]:

	geometry	aeroway	amenity	building	healthcare	historic	landuse	leisure	military	natural	office	shop	sport	tourism	water	waterway
feature_id
node/1847147395	POINT (21.04467 52.27517)	None	None	None	None	None	None	None	None	None	None	convenience	None	None	None	None
node/1847148405	POINT (21.03818 52.2574)	None	pub	None	None	None	None	None	None	None	None	None	None	None	None	None
node/1847148453	POINT (21.03761 52.25714)	None	cafe	None	None	None	None	None	None	None	None	None	None	None	None	None
node/1849727484	POINT (20.9936 52.23076)	None	bank	None	None	None	None	None	None	None	None	None	None	None	None	None
node/1851381884	POINT (21.07414 52.12393)	None	bench	None	None	None	None	None	None	None	None	None	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
way/1119983098	POLYGON ((20.93297 52.29219, 20.93352 52.29205...	None	None	None	None	None	None	track	None	None	None	None	athletics	None	None	None
way/1119983099	POLYGON ((20.93298 52.29221, 20.93297 52.29219...	None	None	None	None	None	None	pitch	None	None	None	None	athletics	None	None	None
way/1119983123	POLYGON ((20.93331 52.28403, 20.93331 52.28397...	None	None	None	None	None	None	pitch	None	None	None	None	athletics	None	None	None
way/1119983308	POLYGON ((20.93749 52.27198, 20.93767 52.2723,...	None	None	None	None	None	None	track	None	None	None	None	athletics	None	None	None
way/1119983309	POLYGON ((20.93748 52.27199, 20.93749 52.27198...	None	None	None	None	None	None	pitch	None	None	None	None	athletics	None	None	None

345780 rows × 16 columns

Plot features¶

Inspired by prettymaps

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clipped_features_gdf = warsaw_features_gdf.clip(warsaw_gdf.geometry.union_all())
clipped_features_gdf = warsaw_features_gdf.clip(warsaw_gdf.geometry.union_all())

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ax = warsaw_gdf.plot(color="lavender", figsize=(16, 16))

# plot water
clipped_features_gdf.dropna(subset=["water", "waterway"], how="all").plot(
    ax=ax, color="deepskyblue"
)

# plot greenery
clipped_features_gdf[
    clipped_features_gdf["landuse"].isin(
        ["grass", "orchard", "flowerbed", "forest", "greenfield", "meadow"]
    )
].plot(ax=ax, color="mediumseagreen")

# plot buildings
clipped_features_gdf.dropna(subset=["building"], how="all").plot(
    ax=ax, color="dimgray", markersize=0.1
)

xmin, ymin, xmax, ymax = warsaw_gdf.total_bounds
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.set_axis_off()
ax = warsaw_gdf.plot(color="lavender", figsize=(16, 16))

# plot water
clipped_features_gdf.dropna(subset=["water", "waterway"], how="all").plot(
    ax=ax, color="deepskyblue"
)

# plot greenery
clipped_features_gdf[
    clipped_features_gdf["landuse"].isin(
        ["grass", "orchard", "flowerbed", "forest", "greenfield", "meadow"]
    )
].plot(ax=ax, color="mediumseagreen")

# plot buildings
clipped_features_gdf.dropna(subset=["building"], how="all").plot(
    ax=ax, color="dimgray", markersize=0.1
)

xmin, ymin, xmax, ymax = warsaw_gdf.total_bounds
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.set_axis_off()

No description has been provided for this image

Download all features from popular tags based on OSMTagInfo in Vienna, Austria¶

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popular_tags = get_popular_tags(in_wiki_only=True)

num_keys = len(popular_tags)
f"Unique keys: {num_keys}."
popular_tags = get_popular_tags(in_wiki_only=True)

num_keys = len(popular_tags)
f"Unique keys: {num_keys}."

Out[5]:

'Unique keys: 376.'

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{k: popular_tags[k] for k in list(popular_tags)[:10]}
{k: popular_tags[k] for k in list(popular_tags)[:10]}

Out[6]:

{'4wd_only': ['yes'],
 'LandPro08:reviewed': ['no'],
 'abandoned': ['yes'],
 'abandoned:railway': ['rail'],
 'abutters': ['residential'],
 'access': ['agricultural',
  'customers',
  'delivery',
  'designated',
  'destination',
  'forestry',
  'military',
  'no',
  'permissive',
  'permit',
  'private',
  'unknown',
  'yes'],
 'access_control': ['full', 'partial'],
 'addr:TW:dataset': ['137998'],
 'addr:city': ['London', '臺北市'],
 'addr:country': ['CZ', 'DE', 'RU', 'TW', 'US']}

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vienna_center_circle = buffer_geometry(Point(16.37009, 48.20931), meters=1000)
vienna_center_circle_gdf = gpd.GeoDataFrame(
    geometry=[vienna_center_circle],
    crs=WGS84_CRS,
    index=gpd.pd.Index(data=["Vienna"], name=REGIONS_INDEX),
)
vienna_center_circle = buffer_geometry(Point(16.37009, 48.20931), meters=1000)
vienna_center_circle_gdf = gpd.GeoDataFrame(
    geometry=[vienna_center_circle],
    crs=WGS84_CRS,
    index=gpd.pd.Index(data=["Vienna"], name=REGIONS_INDEX),
)

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loader = OSMPbfLoader()
vienna_features_gdf = loader.load(vienna_center_circle_gdf, popular_tags)
vienna_features_gdf
loader = OSMPbfLoader()
vienna_features_gdf = loader.load(vienna_center_circle_gdf, popular_tags)
vienna_features_gdf

/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/quackosm/pb
f_file_reader.py:2614: UserWarning: Select clause contains more than 100 columns
(found 376 columns). Query might fail with insufficient memory resources. 
Consider applying more restrictive OsmTagsFilter for parsing.
  warnings.warn(

Finished operation in 0:00:30

Out[8]:

	geometry	access	admin_level	advertising	amenity	area:highway	artwork_type	atm	barrier	bench	...	tunnel	type	usage	vehicle	vending	waste	water	water_source	waterway	wheelchair
feature_id
node/1368442353	POINT (16.37018 48.21749)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
node/1375294847	POINT (16.35947 48.21124)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
node/1375294851	POINT (16.35848 48.21226)	None	None	None	restaurant	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	no
node/1376346269	POINT (16.37881 48.21008)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
node/1376346273	POINT (16.38274 48.2105)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
relation/6155723	POLYGON ((16.36869 48.20096, 16.3688 48.20117,...	None	None	None	None	None	None	None	None	yes	...	yes	multipolygon	None	None	None	None	None	None	None	yes
relation/6528503	POLYGON ((16.37127 48.21546, 16.37135 48.21538...	None	None	None	None	None	None	None	None	None	...	None	multipolygon	None	None	None	None	None	None	None	None
relation/7362473	POLYGON ((16.35977 48.211, 16.35979 48.21101, ...	None	None	None	fountain	None	None	None	None	None	...	None	multipolygon	None	None	None	None	None	None	None	None
relation/6528506	POLYGON ((16.37002 48.21476, 16.36996 48.21474...	None	None	None	None	None	None	None	None	None	...	None	multipolygon	None	None	None	None	None	None	None	None
relation/6528511	POLYGON ((16.37114 48.2149, 16.37116 48.21489,...	None	None	None	None	None	None	None	None	None	...	None	multipolygon	None	None	None	None	None	None	None	None

23285 rows × 194 columns

Plot features¶

Uses default preset colours from prettymaps

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clipped_vienna_features_gdf = vienna_features_gdf.clip(vienna_center_circle)
clipped_vienna_features_gdf = vienna_features_gdf.clip(vienna_center_circle)

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ax = vienna_center_circle_gdf.plot(color="#F2F4CB", figsize=(16, 16))

# plot water
clipped_vienna_features_gdf.dropna(subset=["water", "waterway"], how="all").plot(
    ax=ax, color="#a8e1e6"
)

# plot streets
clipped_vienna_features_gdf.dropna(subset=["highway"], how="all").plot(
    ax=ax, color="#475657", markersize=0.1
)

# plot buildings
clipped_vienna_features_gdf.dropna(subset=["building"], how="all").plot(ax=ax, color="#FF5E5B")

# plot parkings
clipped_vienna_features_gdf[
    (clipped_vienna_features_gdf["amenity"] == "parking")
    | (clipped_vienna_features_gdf["highway"] == "pedestrian")
].plot(ax=ax, color="#2F3737", markersize=0.1)

# plot greenery
clipped_vienna_features_gdf[
    clipped_vienna_features_gdf["landuse"].isin(
        ["grass", "orchard", "flowerbed", "forest", "greenfield", "meadow"]
    )
].plot(ax=ax, color="#8BB174")

xmin, ymin, xmax, ymax = vienna_center_circle_gdf.total_bounds
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.set_axis_off()
ax = vienna_center_circle_gdf.plot(color="#F2F4CB", figsize=(16, 16))

# plot water
clipped_vienna_features_gdf.dropna(subset=["water", "waterway"], how="all").plot(
    ax=ax, color="#a8e1e6"
)

# plot streets
clipped_vienna_features_gdf.dropna(subset=["highway"], how="all").plot(
    ax=ax, color="#475657", markersize=0.1
)

# plot buildings
clipped_vienna_features_gdf.dropna(subset=["building"], how="all").plot(ax=ax, color="#FF5E5B")

# plot parkings
clipped_vienna_features_gdf[
    (clipped_vienna_features_gdf["amenity"] == "parking")
    | (clipped_vienna_features_gdf["highway"] == "pedestrian")
].plot(ax=ax, color="#2F3737", markersize=0.1)

# plot greenery
clipped_vienna_features_gdf[
    clipped_vienna_features_gdf["landuse"].isin(
        ["grass", "orchard", "flowerbed", "forest", "greenfield", "meadow"]
    )
].plot(ax=ax, color="#8BB174")

xmin, ymin, xmax, ymax = vienna_center_circle_gdf.total_bounds
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.set_axis_off()

Download all grouped features based on Geofabrik layers in New York, USA¶

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manhattan_bbox = box(-73.994551, 40.762396, -73.936872, 40.804239)
manhattan_bbox_gdf = gpd.GeoDataFrame(
    geometry=[manhattan_bbox],
    crs=WGS84_CRS,
    index=gpd.pd.Index(data=["New York"], name=REGIONS_INDEX),
)
manhattan_bbox = box(-73.994551, 40.762396, -73.936872, 40.804239)
manhattan_bbox_gdf = gpd.GeoDataFrame(
    geometry=[manhattan_bbox],
    crs=WGS84_CRS,
    index=gpd.pd.Index(data=["New York"], name=REGIONS_INDEX),
)

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loader = OSMPbfLoader()
new_york_features_gdf = loader.load(manhattan_bbox_gdf, GEOFABRIK_LAYERS)
new_york_features_gdf
loader = OSMPbfLoader()
new_york_features_gdf = loader.load(manhattan_bbox_gdf, GEOFABRIK_LAYERS)
new_york_features_gdf

/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/quackosm/osm_extracts/__init__.py:602: GeometryNotCoveredWarning: Skipping extract because of low IoU value (bbbike_newyork, 0.000187).
  warnings.warn(

Finished operation in 0:00:35

Out[12]:

	geometry	accommodation	buildings	catering	education	fuel_parking	health	highway_links	landuse	leisure	...	public	railways	shopping	tourism	traffic	transport	very_small_roads	water	water_traffic	waterways
feature_id
node/462004948	POINT (-73.95553 40.77949)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	railway=station	None	None	None	None
node/480491548	POINT (-73.96358 40.78135)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	highway=crossing	None	None	None	None	None
node/480492539	POINT (-73.96863 40.77474)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	highway=crossing	None	None	None	None	None
node/480492646	POINT (-73.96828 40.77501)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	highway=crossing	None	None	None	None	None
node/480492947	POINT (-73.9665 40.7781)	None	None	None	None	None	None	None	None	None	...	None	None	None	None	highway=crossing	None	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
relation/10925251	POLYGON ((-73.99006 40.77396, -73.98999 40.774...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
relation/9771573	POLYGON ((-73.97507 40.76441, -73.97512 40.764...	tourism=hotel	building=hotel	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
relation/10078100	POLYGON ((-73.99363 40.77349, -73.99362 40.773...	None	None	None	None	None	None	None	landuse=grass	None	...	None	None	None	None	None	None	None	None	None	None
relation/7459068	POLYGON ((-73.97729 40.77214, -73.97732 40.772...	None	None	amenity=restaurant	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None
relation/4623434	POLYGON ((-73.98243 40.77731, -73.98245 40.777...	None	None	None	None	None	None	None	None	None	...	None	None	None	None	None	None	None	None	None	None

50810 rows × 27 columns

Plot features¶

Inspired by https://snazzymaps.com/style/14889/flat-pale

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ax = manhattan_bbox_gdf.plot(color="#e7e7df", figsize=(16, 16))

# plot greenery
new_york_features_gdf[new_york_features_gdf["leisure"] == "leisure=park"].plot(
    ax=ax, color="#bae5ce"
)

# plot water
new_york_features_gdf.dropna(subset=["water", "waterways"], how="all").plot(ax=ax, color="#c7eced")

# plot streets
new_york_features_gdf.dropna(subset=["paths_unsuitable_for_cars"], how="all").plot(
    ax=ax, color="#e7e7df", linewidth=1
)
new_york_features_gdf.dropna(
    subset=["very_small_roads", "highway_links", "minor_roads"], how="all"
).plot(ax=ax, color="#fff", linewidth=2)
new_york_features_gdf.dropna(subset=["major_roads"], how="all").plot(
    ax=ax, color="#fac9a9", linewidth=3
)

# plot buildings
new_york_features_gdf.dropna(subset=["buildings"], how="all").plot(ax=ax, color="#cecebd")

xmin, ymin, xmax, ymax = manhattan_bbox_gdf.total_bounds
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.set_axis_off()
ax = manhattan_bbox_gdf.plot(color="#e7e7df", figsize=(16, 16))

# plot greenery
new_york_features_gdf[new_york_features_gdf["leisure"] == "leisure=park"].plot(
    ax=ax, color="#bae5ce"
)

# plot water
new_york_features_gdf.dropna(subset=["water", "waterways"], how="all").plot(ax=ax, color="#c7eced")

# plot streets
new_york_features_gdf.dropna(subset=["paths_unsuitable_for_cars"], how="all").plot(
    ax=ax, color="#e7e7df", linewidth=1
)
new_york_features_gdf.dropna(
    subset=["very_small_roads", "highway_links", "minor_roads"], how="all"
).plot(ax=ax, color="#fff", linewidth=2)
new_york_features_gdf.dropna(subset=["major_roads"], how="all").plot(
    ax=ax, color="#fac9a9", linewidth=3
)

# plot buildings
new_york_features_gdf.dropna(subset=["buildings"], how="all").plot(ax=ax, color="#cecebd")

xmin, ymin, xmax, ymax = manhattan_bbox_gdf.total_bounds
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.set_axis_off()

Using OSMPbfLoader to download data for a specific area and transforming it to GeoParquet file¶

Download all grouped features based on Geofabrik layers in Reykjavík, Iceland¶

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loader = OSMPbfLoader()
reykjavik_gdf = geocode_to_region_gdf("Reykjavík, IS")
reykjavik_features_gpq = loader.load_to_geoparquet(reykjavik_gdf, GEOFABRIK_LAYERS)
reykjavik_features_gpq
loader = OSMPbfLoader()
reykjavik_gdf = geocode_to_region_gdf("Reykjavík, IS")
reykjavik_features_gpq = loader.load_to_geoparquet(reykjavik_gdf, GEOFABRIK_LAYERS)
reykjavik_features_gpq

Finished operation in 0:00:23

Out[14]:

PosixPath('files/27255c31a829630e955fdd267713c8cf23ae8384764ce0f9d850bad99fee31a1_098931824b94bfe02e01bc4987422a39ec47bd3f6924e5325ddae0846badafba_exploded.parquet')

Read those features using DuckDB¶

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import duckdb

connection = duckdb.connect()

connection.load_extension("parquet")
connection.load_extension("spatial")

features_relation = connection.read_parquet(str(reykjavik_features_gpq))
features_relation
import duckdb

connection = duckdb.connect()

connection.load_extension("parquet")
connection.load_extension("spatial")

features_relation = connection.read_parquet(str(reykjavik_features_gpq))
features_relation

Out[15]:

┌─────────────────┬───────────────┬─────────────┬───────────┬──────────┬───────────┬──────────────┬─────────┬───────────────┬─────────┬─────────┬─────────────┬─────────────┬───────────────┬─────────┬─────────┬───────────────────────────┬─────────┬─────────┬──────────┬──────────┬─────────────────────┬─────────────────────┬───────────┬──────────────────┬─────────┬───────────────┬───────────┬────────────────────────────────┐
│   feature_id    │ accommodation │ air_traffic │ buildings │ catering │ education │ fuel_parking │ health  │ highway_links │ landuse │ leisure │ major_roads │ minor_roads │    miscpoi    │  money  │ natural │ paths_unsuitable_for_cars │  pofw   │ public  │ railways │ shopping │       tourism       │       traffic       │ transport │ very_small_roads │  water  │ water_traffic │ waterways │            geometry            │
│     varchar     │    varchar    │   varchar   │  varchar  │ varchar  │  varchar  │   varchar    │ varchar │    varchar    │ varchar │ varchar │   varchar   │   varchar   │    varchar    │ varchar │ varchar │          varchar          │ varchar │ varchar │ varchar  │ varchar  │       varchar       │       varchar       │  varchar  │     varchar      │ varchar │    varchar    │  varchar  │            geometry            │
├─────────────────┼───────────────┼─────────────┼───────────┼──────────┼───────────┼──────────────┼─────────┼───────────────┼─────────┼─────────┼─────────────┼─────────────┼───────────────┼─────────┼─────────┼───────────────────────────┼─────────┼─────────┼──────────┼──────────┼─────────────────────┼─────────────────────┼───────────┼──────────────────┼─────────┼───────────────┼───────────┼────────────────────────────────┤
│ node/292737099  │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ tourism=information │ NULL                │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.7161219 64.2090936) │
│ node/292737101  │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ tourism=information │ NULL                │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.7157757 64.2091298) │
│ node/292737862  │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ tourism=viewpoint   │ NULL                │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.7129395 64.2320585) │
│ node/292767131  │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=crossing    │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.826188 64.1099305)  │
│ node/292770701  │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ tourism=information │ NULL                │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.808829 64.102486)   │
│ node/292770708  │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ amenity=bench │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ NULL                │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8089259 64.102549)  │
│ node/292771780  │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ amenity=bench │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ NULL                │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.7982196 64.0975048) │
│ node/292771781  │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ amenity=bench │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ NULL                │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.798272 64.0974945)  │
│ node/292773105  │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=crossing    │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.7931994 64.0976202) │
│ node/292773108  │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=crossing    │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.7928774 64.0976526) │
│       ·         │  ·            │  ·          │  ·        │  ·       │  ·        │  ·           │  ·      │  ·            │  ·      │  ·      │  ·          │  ·          │  ·            │  ·      │  ·      │  ·                        │  ·      │  ·      │  ·       │  ·       │  ·                  │        ·            │  ·        │  ·               │  ·      │  ·            │  ·        │               ·                │
│       ·         │  ·            │  ·          │  ·        │  ·       │  ·        │  ·           │  ·      │  ·            │  ·      │  ·      │  ·          │  ·          │  ·            │  ·      │  ·      │  ·                        │  ·      │  ·      │  ·       │  ·       │  ·                  │        ·            │  ·        │  ·               │  ·      │  ·            │  ·        │               ·                │
│       ·         │  ·            │  ·          │  ·        │  ·       │  ·        │  ·           │  ·      │  ·            │  ·      │  ·      │  ·          │  ·          │  ·            │  ·      │  ·      │  ·                        │  ·      │  ·      │  ·       │  ·       │  ·                  │        ·            │  ·        │  ·               │  ·      │  ·            │  ·        │               ·                │
│ node/1704980057 │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=street_lamp │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8479983 64.1244886) │
│ node/1704980060 │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=street_lamp │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8484328 64.1247531) │
│ node/1704980062 │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=street_lamp │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8508353 64.124079)  │
│ node/1704980064 │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=street_lamp │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8481432 64.1230417) │
│ node/1704980065 │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=street_lamp │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8480841 64.122501)  │
│ node/1704980067 │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=street_lamp │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8489478 64.1232128) │
│ node/1704980069 │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=street_lamp │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8506153 64.1237581) │
│ node/1704980075 │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=street_lamp │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8482504 64.1232712) │
│ node/1704980078 │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=street_lamp │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.847891 64.1241749)  │
│ node/1704980081 │ NULL          │ NULL        │ NULL      │ NULL     │ NULL      │ NULL         │ NULL    │ NULL          │ NULL    │ NULL    │ NULL        │ NULL        │ NULL          │ NULL    │ NULL    │ NULL                      │ NULL    │ NULL    │ NULL     │ NULL     │ NULL                │ highway=street_lamp │ NULL      │ NULL             │ NULL    │ NULL          │ NULL      │ POINT (-21.8508728 64.1243809) │
├─────────────────┴───────────────┴─────────────┴───────────┴──────────┴───────────┴──────────────┴─────────┴───────────────┴─────────┴─────────┴─────────────┴─────────────┴───────────────┴─────────┴─────────┴───────────────────────────┴─────────┴─────────┴──────────┴──────────┴─────────────────────┴─────────────────────┴───────────┴──────────────────┴─────────┴───────────────┴───────────┴────────────────────────────────┤
│ ? rows (>9999 rows, 20 shown)                                                                                                                                                                                                                                                                                                                                                                                              29 columns │
└───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

Count all buildings¶

In [16]:

Copied!

features_relation.filter("buildings IS NOT NULL").count("feature_id")
features_relation.filter("buildings IS NOT NULL").count("feature_id")

Out[16]:

┌───────────────────┐
│ count(feature_id) │
│       int64       │
├───────────────────┤
│             25370 │
└───────────────────┘

You can see more examples of how to use PbfFileReader from the QuackOSM library in the docs: https://kraina-ai.github.io/quackosm/latest/examples/pbf_file_reader/

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Using OSMPbfLoader to download data for a specific area and transforming it to GeoParquet file¶

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Count all buildings¶

Download all features from `HEX2VEC_FILTER` in Warsaw, Poland¶