Regrid Matched Building Footprints Frequently Asked Questions

What is the source of the buildings data?

Our partners at EarthDefine use high-resolution National Agriculture Imagery Program (NAIP) imagery. EarthDefine then applies their industry-leading AI models to the imagery to derive high-quality nationwide building footprints.

What is the geographic coverage of the data?

The buildings data covers the contiguous United States (the “lower 48”). It does not include building footprints for Alaska, Hawaii, or territories.

Building footprints are included for all counties, even when Regrid parcel data is not avalable in those counties.

What is the quality of the imagery?

NAIP provides 60cm or higher resolution color infrared imagery across the lower 48 States.

NAIP data is flown statewide by different contractors, so every state might have a different NAIP vendor, and there is some variability within the nationwide imagery.

How accurate is the data?

In terms of spatial accuracy, the georectification standard of the NAIP imagery is +/- 6 meters, and we have found it to be better in many places. This number is the 95% confidence level (CE95), meaning that “95% of well-defined points tested shall fall within 6 meters of true ground”. The spatial accuracy of the derived buildings is at least +/- 6m, or at worst 6 meters.

To find the accuracy of the buildings themselves, we use a metric called Intersection over Union (IoU) to evaluate how well buildings line up with validation data. The current IoU for the buildings is over .94, which is quite good. For comparison, the Microsoft Buildings product has an IoU of 0.86.

This data goes through multiple rounds of quality control to ensure that discrepancies like shadows, incorrect building edges are removed when classifying and obtaining footprints.

How often is the data updated?

You can expect an rolling updates multiple times per year.

The buildings data is mainly based on the currency of the NAIP imagery. Half of the country usually has NAIP imagery updates once per year, and many states do updates every year. The latest NAIP imagery is used by EarthDefine to update the data at least once a year.

Our partners at EarthDefine are constantly updating and improving their algorithms to improve the quality of the data. These improvements are also released as updates throughout the year. We refer to this in the buildings schema as ed_source_date, and on our data store as “Building Source Imagery Date.”

Why do some geographies have older data?

Occasionally, NAIP imagery does not meet EarthDefine’s quality standards or is of lower quality than the previous year’s imagery. In these cases, older imagery may be used to derive higher quality footprints.

What is the join file?

We provide a join file that matches parcels by ll_uuid to buildings by their ed_bld_uuid. One parcel may have multiple buildings on it, and one building may span multiple parcels. The Regrid Matched Building Footprints Join File accounts for this many-to-many relationship. We have tuned an algorithm that accounts for variance in the data to provide building to parcel matches that are more accurate than a typical spatial intersction query. More details about the join file structure are available here.

Do you provide the matched data via API?

We offer an upgraded API option that returns parcles + matched building footprints via the Regrid Parcel API, please review our Regrid Parcel API options page for more details.

Is building square footage or height provided?

Building footprint square footage is provided, but height, stories, and total building square footage are not currently included.

How does a ed_str_uuid differ from a ed_bld_uuid?

We deliver the spatial data at the building level. Every shape in the buildings data has a ed_bld_uuid. Because the individual building level is much more spatially precise, our join table makes use of the ed_bld_uuid and we expect most people to make use of the ed_bld_uuid in their workflows and code.

Buildings may be derived from a larger structure, identified by the ed_str_uuid. For example, a large row of homes may appear in imagery data as one large structure, but in reality it is many smaller buildings.