Building footprints are one of the most commonly used datasets in GIS. As polygon vectors representing the spatial extent of a physical structure, building footprint data provides important context for basemaps, insurance risk assessment, climate resilience analysis, and many other geospatial applications.

Understanding exactly where entire buildings are located - not just points representing their addresses - is critical for analyzing how they relate to both the natural and built environment. Consider a single-family home; its risk profile is not only associated with flood zones, tree canopy, and other natural hazards, but also neighboring properties. What’s more, properties often include multiple structures, such as sheds, barns, or detached garages, which cannot be accurately represented with point data alone.

Property analytics using building footprint data are frequently performed by a wide variety of public and private sector organizations seeking to better understand our dynamically changing world. For example, property and casualty (P&C) insurance carriers rely on building footprint data to accurately assess risk and price policies accordingly. Similarly, government agencies leverage building footprints to analyze how the location of infrastructure impacts natural hazard risk, conservation efforts, and other urban planning considerations. Telecommunications professionals identify broadband-serviceable locations (BSLs) using building footprints, and humanitarian organizations utilize building footprints to estimate populations and optimize resource allocation. 

As there are many applications for building footprints, there are also a number of data providers for users to choose from. However, each provider’s dataset varies in quality, so it’s important to know how you want to use building footprint data and make sure to select a dataset that meets your needs.

The importance of high-quality building footprint data

High-precision building footprint data is integral to many types of geospatial analyses because they provide authoritative information about the exact spatial extent of structures. Although many data providers claim to offer quality building footprints, many lack the completeness, currency, and accuracy required for true property intelligence and risk assessment. 

At Ecopia AI (Ecopia), our entire focus is on leveraging our advanced AI-based mapping systems to extract high-precision vector data from geospatial imagery. Each year, we ingest the most recent high-resolution imagery from our partner network to produce the first and only complete map of buildings in the US. This dataset is currently in use by over 100 organizations spanning insurance, government, telecommunications, and other industries requiring comprehensive, accurate, and up-to-date building footprint data.

To demonstrate the importance of high-precision building footprint data and how quality can vary by provider, we compared Ecopia building footprints in Sarasota County, Florida with data from Microsoft and OpenStreetMaps (OSM). Two vintages of Ecopia data are used (2022 and 2023) to show year-over-year change, in addition to the most recent Microsoft (2019-2020) and OSM (2024) datasets. To show how different building footprint datasets can impact property risk assessment, we also layer in FEMA flood zone data.

See how the datasets compare in this interactive map:

Building footprint data completeness

When evaluating a building footprint data provider, a key quality criterion to keep in mind is how complete or comprehensive the dataset is. In other words, are all buildings present, or are some missing? When comparing a vector dataset to current geospatial imagery, it’s pretty easy to see how complete the data is. However, seeing the feature count can also be helpful. 

Incomplete building footprint data can result in inaccurate analytics, miscalculated risk, and similar issues. For instance, a government organization may not fully recognize natural hazard risk in a given area if not all structures are represented, and an insurance carrier may underprice a policy if the neighboring property is not properly accounted for.

Building footprint data currency

Another important data quality metric to consider during an evaluation is currency. The world changes every day, and buildings are some of the most dynamically changing elements in society. Every day buildings are created, modified, or demolished, changes that not all data providers account for in their building footprint datasets.

To show just how much can change in a single year, consider this comparison of Ecopia’s building footprints in Sarasota County from 2022 and 2023.

This analysis shows that in just one single county, a net positive difference of 7,435 buildings occurred over the course of a year, which is why it is so important to refresh building footprint data frequently. A similar analysis could not be performed with Microsoft or OSM data given the lack of access to regular updates.

At Ecopia, we update our full US building footprint data annually, and can produce even more frequent updates if needed. We also specialize in change detection mapping to produce vector datasets with unique attribution that highlights exactly which features have been altered and how over a given time period.

Building footprint data accuracy

While completeness and currency are extremely important, quality data must also be accurate to be useful. Building footprint data accuracy can be impacted by a few different factors. For example, if building polygons are not digitized correctly, they do not represent the true spatial extent of the structure. Similarly, if non-building elements are misclassified as buildings, or buildings are missing, the dataset is inaccurate.

Inaccurate building footprint data can lead to incorrect results from analytics, which can then cause errors in decision-making. Using the FEMA flood zones from our map visualization, we can quantify how flood risk analysis can differ based on building footprint data accuracy.

To further quantify how inaccurate building footprint shapes and counts impact analysis results, we can compare the total square footage of building footprints located in a flood zone for each data provider. These vastly different numbers emphasize how drastically property risk analytics can change based on the building footprint data used.

Get started with Ecopia AI building footprints

Whether you’re analyzing community vulnerability to natural hazards, pricing insurance policies, or performing another geospatial workflow, it’s important to use building footprint data you can trust as an input. If you’re ready to start using the highest quality building footprints available, get in touch with our team.