This AI Pack can be purchased as an add-on to the Building Footprints pack. It appears for the first time with Gen 3 data, and adds metadata to the building footprints for:

  • The "Peak Height" of the building
  • The number of storeys of the building

Output from the AI Feature API building footprint data, with Gen 3 "building characteristics" overlaid automatically using a GIS tool.

3D Building Characteristics

The 3D building characteristics are derived using Nearmap's in house 3D data. For Gen 3 onwards, they are calculated by finding the closest date of mesh to the "survey date" which is being executed. As not all our surveys take 3D imagery, the 3D date might not be identical to the survey date the images/AI are getting pegged against. Where a 3D enabled survey has been flown, this is usually the 3D produced on the same survey.  On non-3D surveys, the next best 3D information will be used within approximately a 12-month window. For offline delivery or via the AI Feature API, this ensures that you will receive 3D attributes on the majority of processed surveys .

Some Gen 3 building footprints in Phoenix, AZ overlaid on our webtile imagery, and extruded using the Peak Height in a GIS application.

Definition - Peak Height

This is the height from a ground point nearby the building, to the 95th percentile of the roof height. This is essentially the ground to the peak of the roof, with some robustness to protrusions or noise that may be attached to the roof.

Definition - Storeys

A ‘storey’ is a building level that is above ground and enclosed by walls. Where the top storey is a partial storey, the algorithm should detect it as two storeys. A slightly elevated building with some crawlspace under the building still only counts as a single storey building. A storey can include rooms, garages: any space that is part of the dwelling.

A difficult example - 2 storeys on the right-hand side, and one storey on the left. The algorithm should class this as a 2 storey building.


These buildings are technically single storey, as the space below them is not habitable.



These buildings are two storey, because they contain components which have two enclosed, habitable floors.



The storey count is a categorical multi-class attribute,  with values and confidences returned for 1, 2 or 3+ storeys. This is not simply an approximation based on Peak Height - we apply machine learning algorithms to our 3D models, in order to accurately assess the number of storeys. The confidences sum to 100% and usually the value with the highest confidence is correct. e.g. if the results are 1 - 90%, 2 - 5%, 3+ - 5%, there is a 90% chance it is a single storey building. In situations where the confidence is split fairly evenly between two storeys (such as 1 - 39%, 2 - 41%, 3+ - 20%), it may indicate that the building has a half storey (in this case it may be a single storey base, with an attic, or partial second storey that does not cover the whole building).

This graph shows how the height in metres is related to the number of storeys, but is not simply a cutoff. We apply machine learning algorithms that consider the geometry of the building as a whole, not just the peak height. Data taken from an area within Phoenix, Arizona.

Version History

For a precise changelog, refer to the AI Generations  pages. This is a convenient summary to describe changes to this AI Pack over time:

Gen 1-2

Not available

Gen 3

Pack first introduced, with attributes as described above (1,2,3+ storeys, peak height).