This AI Pack can be purchased as an add-on to the Residential Building Footprints pack. It adds AI Layers and AI Parcel outputs related to:
AI Feature API
(Gen 1 & 2 Data)
Metadata attached to Roof Feature Class
Attributes of each Building Footprint: Dominant Roof Material, presence of each roof shape.
Tree Overhang presence; area estimate and polygon of each element of overhang.
If you subscribe to Roof Characteristics or Building Characteristics, you must also subscribe to Building Footprints.
3D Roof Characteristics
In Gen 3 data, a new attribute was introduced - Dominant Roof Pitch. This is calculated from our 3D data using a similar approach to the 3D attributes in AI Pack: Building Characteristics. See the description there for when and how the 3D related attributes are made available.
Dominant Roof Pitch
Measured in degrees from horizontal, this is the pitch of the largest area of the roof. It is calculated by assessing the surface normals of the roof area, and identifying the most prevalent surface normal. Where multiple pitches have been used in a roof, this will NOT provide an average - it will be (approximately) the most commonly appearing pitch by area.
Roof Material includes a group of three AI Layers:
It also includes a "dominant roof material" category in AI Parcel exports (both as a column in the spreadsheet and as an attribute associated with a building footprint polygon in the GeoPackage file), which can take the value of "Tile", "Metal", "Shingle" or "Other".
The Dominant roof material is the material taking up the largest horizontal area of the roof (as long as it covers at least 50% of the total area). So a mostly tile roof with a metal extension would be denoted as tile. If there is no clear dominant roof material, it is classified as "Other".
Characteristics and Recommended Use
The dominant roof material is provided to avoid confusion in the very common situation where a shingle or tile roof has a small metal extension. In some situations (due to lighting conditions for example) it can be very difficult to tell the difference between tile and shingle, or between metal and other fairly featureless roof types. This shows up as less bright areas in the AI Layer viewer. The dominant roof material simplifies this information by making the best choice of overall roof material given the information available.
Tiles are much less common in certain parts of the US, which means that the majority of roofs identified as tile are lower confidence. This is because a certain percentage of shingle roofs will always be mistaken for tile, and an absence of actual tile roofs means that these errors form a much larger percentage of those detected as tile.
Shingles are almost never used in Australia whereas they are the most common roof type in most parts of the US. This means that the majority of roofs in Australia identified as shingle are lower confidence. This is because a certain percentage of tile roofs will always be mistaken for shingle, and an absence of actual shingle roofs means that these errors form a much larger percentage of those detected as shingle.
The practical performance of the metal roof attribute varies with region, depending on how prevalent it is, and how common other roof materials are.
The lower number of "Other" roof material in Australia means that less of the "Other" predictions are confident (as Australia is almost exclusively tile, metal and concrete roofs).
The set of roof shape attributes each detects whether there is at least one structural element of the roof with one of these definitions.
Tree Overhang can be defined as any case where there is some tree (leaf on or off) protruding over the top of a Building Footprint. A Tree Overhang AI Layer is available for viewing in MapBrowser . Polygons of tree overhang, as well as area estimates, are available in an AI Parcel export.
Characteristics and Recommended Use
When presented as a "Y" or "N" flag in the CSV file, Tree Overhang may not be directly useful. It detects any amount of overlap at all, such that more than half of the properties in our coverage area are listed as having tree overhang. Where the overhang is used to identify trees that may need trimming, or calculating property risk, it is recommended you use the area estimate to set a meaningful threshold (e.g. only look at buildings with >10m2 of overhang). In more advanced use cases, you could use the area directly to rank properties from highest to lowest amount of overhang, or as a continuous variable to use in a statistical model. It is also possible to consider the shape and position of the tree overhang sections on the building, obtained from the geospatial file of exported AI Parcels.
The confidence values for Tree Overhang are particularly low, because the edges of trees that overhang a roof often fade out gradually between high confidence and very low confidence. The area estimate of Tree Overhang is recommended as a better means of adjusting whether mild, moderate or extreme overhang should be included for your particular application.
Leaf-off Vegetation Overhang
Leaf-off Vegetation Overhang is very similar to Tree Overhang in that it intersects the roof outline with the Leaf-off Vegetation layer. There are several key use cases for this data.
- In areas with seasonal vegetation (deciduous trees), leaf-off tree overhang indicates the roof has been captured on a date that is optimal for recovering the correct roof outline (as it is usually more visible through bare branches).
- In areas or seasons where all trees are expected to have leaves, it can indicate the overhang of dead or unhealthy tree that presents different risks to a building.