To enable this capability, GIS programs have developed sophisticated tools for digitizing imagery into vector features. However, manually digitizing features remains a time-consuming and tedious process that is almost impossible to scale - not to mention expensive.
Ecopia’s building footprint methodology
Our team recognized the challenges organizations encounter when faced with digitizing buildings at scale, so we developed an artificial intelligence (AI)-based mapping system for detecting, extracting, and updating geospatial features from imagery data. Using this methodology, we have been able to create high-precision maps of geographic areas ranging in size from entire continents down to granular cityscapes, in both 2D and 3D.
Developing building polygon data from imagery is complex, but we have been able to help organizations rapidly scale their analysis by taking on the heavy lifting of sourcing and maintaining an accurate database. Our collaborative partner network allows us to digitize the best imagery data, ensuring our AI is constantly updating to reflect dynamic real-world change.
Top use cases for building footprints
So what can building polygons be used for? Each day there is a new and innovative use case for building footprint data, but at Ecopia we tend to see them used to solve the following spatial challenges.
Building polygons for insurance risk assessment
When pricing a policy for property insurance, underwriters must calculate the risk of a specific property and structure. Oftentimes, this is done by measuring a building’s proximity to a flood zone, or identifying whether or not a property is adjacent to a high-risk business (such as an industrial plant containing flammable chemicals). But regardless of what underwriters are measuring the risk of, what does not change is the importance of correctly locating the property’s exact extent so as not to under- or over-price a policy.
This need for precision is what makes building footprints an integral part of insurance underwriting. While singular latitude and longitude point coordinates can represent the approximate location of a property, they do not provide the level of detail needed to understand how the building itself relates to nearby risks.