Building Footprints Dataset¶

The Data Services and Engineering team maintains a derived dataset from the Microsoft US Building Footprints and Global ML Building Footprints datasets. The two datasets are broadly similar, but the latter has global coverage and is more frequently updated.

We take the original datasets, and join them with US Census TIGER data to make them more useful for demographic and social science research. The additional census-derived fields include:

State
County
Tract
Block Group
Block
Place

If a footprint intersects more than one of the above, we assign it to the one with the greater intersection, so each footprint should only appear once in the dataset.

Note

Despite the names, these derived datasets are scoped to California only.

Usage¶

The data are stored as files in AWS S3. We distribute them in both GeoParquet and zipped Shapefile formats.

GeoParquet is usually a superior format for doing data analytics as it is:

An open format, based on the industry-standard Parquet format.
Efficiently compressed
Cloud-native
Uses a columnar data layout optimized for analytical workloads.

However, GeoParquet is also somewhat newer, and not supported by all tooling yet, so the zipped Shapefiles may be better suited for some workflows (especially Esri ones).

Usage with GeoPandas¶

GeoPandas is an extension to the Python Pandas library enabling analysis of geopsatial vector data.

Examples for reading the files using GeoPanas:

import os
import geopandas

# Ensure S3 requests are anonymous, there is no need for AWS credentials here.
os.environ["AWS_NO_SIGN_REQUEST"] = "YES"

# Read GeoParquet using S3 URL
gdf = geopandas.read_parquet(
    "s3://dof-demographics-dev-us-west-2-public/"
    "global_ml_building_footprints/parquet/county_fips_003.parquet"
)

# Read Shapefile using HTTPS URL
gdf = geopandas.read_file(
    "https://dof-demographics-dev-us-west-2-public.s3.us-west-2.amazonaws.com/"
    "global_ml_building_footprints/shp/county_fips_003.zip"
)

Usage with ArcGIS Pro toolbox:¶

Fennis Reed at the California Department of Finance Demographics Research Unit has created an ArcGIS Pro toolbox for downloading individual footprint files, which can be downloaded here. Some usage notes for the toolbox are here

Removing errors of inclusion:¶

Fennis Reed has also constructed a dataset of known "errors of inclusion", i.e. shapes that were incorrectly identified as footprints by the machine learning algorithm. These errors of inclusion are updated in this gist, and examples for how to use them to filter them from other datasets can be found in this gist.

The above ArcGIS Pro toolbox also includes an option for filtering errors of inclusion using the same process.

Links¶

The following tables contains public links to the datasets partitioned by county. The HTTPS URLs can be used to directly download files using a web browser, while the S3 URLs are more appropriate for scripts like the examples above.

Global ML Building Footprints¶

County	GeoParquet (HTTPS)	Shapefile (HTTPS)	GeoParquet (S3)	Shapefile (S3)
Alameda	URL	URL	URL	URL
Alpine	URL	URL	URL	URL
Amador	URL	URL	URL	URL
Butte	URL	URL	URL	URL
Calaveras	URL	URL	URL	URL
Colusa	URL	URL	URL	URL
Contra Costa	URL	URL	URL	URL
Del Norte	URL	URL	URL	URL
El Dorado	URL	URL	URL	URL
Fresno	URL	URL	URL	URL
Glenn	URL	URL	URL	URL
Humboldt	URL	URL	URL	URL
Imperial	URL	URL	URL	URL
Inyo	URL	URL	URL	URL
Kern	URL	URL	URL	URL
Kings	URL	URL	URL	URL
Lake	URL	URL	URL	URL
Lassen	URL	URL	URL	URL
Los Angeles	URL	URL	URL	URL
Madera	URL	URL	URL	URL
Marin	URL	URL	URL	URL
Mariposa	URL	URL	URL	URL
Mendocino	URL	URL	URL	URL
Merced	URL	URL	URL	URL
Modoc	URL	URL	URL	URL
Mono	URL	URL	URL	URL
Monterey	URL	URL	URL	URL
Napa	URL	URL	URL	URL
Nevada	URL	URL	URL	URL
Orange	URL	URL	URL	URL
Placer	URL	URL	URL	URL
Plumas	URL	URL	URL	URL
Riverside	URL	URL	URL	URL
Sacramento	URL	URL	URL	URL
San Benito	URL	URL	URL	URL
San Bernardino	URL	URL	URL	URL
San Diego	URL	URL	URL	URL
San Francisco	URL	URL	URL	URL
San Joaquin	URL	URL	URL	URL
San Luis Obispo	URL	URL	URL	URL
San Mateo	URL	URL	URL	URL
Santa Barbara	URL	URL	URL	URL
Santa Clara	URL	URL	URL	URL
Santa Cruz	URL	URL	URL	URL
Shasta	URL	URL	URL	URL
Sierra	URL	URL	URL	URL
Siskiyou	URL	URL	URL	URL
Solano	URL	URL	URL	URL
Sonoma	URL	URL	URL	URL
Stanislaus	URL	URL	URL	URL
Sutter	URL	URL	URL	URL
Tehama	URL	URL	URL	URL
Trinity	URL	URL	URL	URL
Tulare	URL	URL	URL	URL
Tuolumne	URL	URL	URL	URL
Ventura	URL	URL	URL	URL
Yolo	URL	URL	URL	URL
Yuba	URL	URL	URL	URL

US Building Footprints¶

County	GeoParquet (HTTPS)	Shapefile (HTTPS)	GeoParquet (S3)	Shapefile (S3)
Alameda	URL	URL	URL	URL
Alpine	URL	URL	URL	URL
Amador	URL	URL	URL	URL
Butte	URL	URL	URL	URL
Calaveras	URL	URL	URL	URL
Colusa	URL	URL	URL	URL
Contra Costa	URL	URL	URL	URL
Del Norte	URL	URL	URL	URL
El Dorado	URL	URL	URL	URL
Fresno	URL	URL	URL	URL
Glenn	URL	URL	URL	URL
Humboldt	URL	URL	URL	URL
Imperial	URL	URL	URL	URL
Inyo	URL	URL	URL	URL
Kern	URL	URL	URL	URL
Kings	URL	URL	URL	URL
Lake	URL	URL	URL	URL
Lassen	URL	URL	URL	URL
Los Angeles	URL	URL	URL	URL
Madera	URL	URL	URL	URL
Marin	URL	URL	URL	URL
Mariposa	URL	URL	URL	URL
Mendocino	URL	URL	URL	URL
Merced	URL	URL	URL	URL
Modoc	URL	URL	URL	URL
Mono	URL	URL	URL	URL
Monterey	URL	URL	URL	URL
Napa	URL	URL	URL	URL
Nevada	URL	URL	URL	URL
Orange	URL	URL	URL	URL
Placer	URL	URL	URL	URL
Plumas	URL	URL	URL	URL
Riverside	URL	URL	URL	URL
Sacramento	URL	URL	URL	URL
San Benito	URL	URL	URL	URL
San Bernardino	URL	URL	URL	URL
San Diego	URL	URL	URL	URL
San Francisco	URL	URL	URL	URL
San Joaquin	URL	URL	URL	URL
San Luis Obispo	URL	URL	URL	URL
San Mateo	URL	URL	URL	URL
Santa Barbara	URL	URL	URL	URL
Santa Clara	URL	URL	URL	URL
Santa Cruz	URL	URL	URL	URL
Shasta	URL	URL	URL	URL
Sierra	URL	URL	URL	URL
Siskiyou	URL	URL	URL	URL
Solano	URL	URL	URL	URL
Sonoma	URL	URL	URL	URL
Stanislaus	URL	URL	URL	URL
Sutter	URL	URL	URL	URL
Tehama	URL	URL	URL	URL
Trinity	URL	URL	URL	URL
Tulare	URL	URL	URL	URL
Tuolumne	URL	URL	URL	URL
Ventura	URL	URL	URL	URL
Yolo	URL	URL	URL	URL
Yuba	URL	URL	URL	URL