To ensure that our performance scores are as objective as possible, our examples are drawn from a statistically determined sample across our coverage regions that is weighted towards populated areas. We have deliberately chosen a significant portion of our examples as challenging cases where our models are least certain.
Our team of highly trained expert labelers use a custom version of MapBrowser to check multiple dates, multiple angles, and even our 3D models to determine whether they believe an object is present. For example, a swimming pool missed on a leaf-on survey will be marked as incorrect if the labeler can see the pool before or after that point in our capture history.
Factoring in errors and inconsistencies
As with most data sets, Nearmap AI data may contain errors or be inconsistent for the following practical reasons:
- Inconsistencies in third party parcel boundaries causing an object to be misidentified as belonging to a neighboring property.
- Definitional differences, where your working definition is subtly different from ours (such as our current Building Footprint definition excluding rooftop car parks).
- Errors in flagging parcels or missing to flag parcels. This is most noticeable for the Construction Site class, where the precision is decreased by picking up things such as landscaper's yards full of rubble and trucks, and the recall is decreased mostly by examples of the first stage of construction, which is usually just an area of dirt with no obvious construction occurring.
Because there's a chance for errors to creep in, we provide you with the ability to view AI Layers in MapBrowser. Nearmap AI gives you a unique perspective of the truth on the ground based on visual data, with different strengths and weaknesses to other sources (such as paper records of construction or solar installations). The consistent behavior makes it an excellent source of truth as a standalone data product. If you have an existing data set representing a different perspective, we recommend that you combine it with ours to achieve a much more accurate picture.