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About AI Generations
As we continue to innovate and improve Nearmap AI, we mark major changes in performance and capability with a new "generation" of content. This section provides a description of the new performance, features and content introduced with each generation of Nearmap AI data. Multiple generations will be available for export at…
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Generation 6 AI Content
50+ new layers Nearmap has enhanced its machine learning capabilities significantly, resulting in the addition of over 50 new AI layers in Gen 6. This integration results in a total of 130+ AI layers, enriching our location intelligence services across all verticals to: Allow remote inspection and assessment Classify and…
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Generation 5 AI Content
Overview Gen 5 introduces a large number of new AI Layers, which brings the total number of raster layers to 80! This has resulted in a similar number of vectorized layers being available. The Gen 5 training dataset has increased by 2.6 times and has been focused on reducing uncertainty. We have also introduced the concept…
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Generation 4 AI Content
Overview Gen 4 is powered by a new raster model with the addition of eight new AI Layers for roof condition and construction. Gen 4 also improves existing layers already available in Gen 3. Open World vectorization has been expanded, enhancing some existing AI Packs and adding new ones. Gen 4 processing includes every…
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Generation 3 AI Content
Overview Gen 3 uses the same raster AI Layers as Gen 2, but with a fundamentally new vectorization process that we call "Open World". At the most basic level, Open World vectorizes all features, regardless of parcel boundaries, allowing us to remove a number of caveats around large or missing parcels, buildings being cut…
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Superseded Generations
The generations described in this article have been superseded by newer AI generation content. Generation 2 AI content Generation 1 AI content Generation 2 AI content Nearmap Gen 2 AI content incorporates the USA Spring 2020 and AU 2020 data refresh. Key features: Highly customized, multi-scale semantic segmentation…