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Use AI Data Effectively
Nearmap derives its AI data from the latest available aerial imagery, which is consistent across our coverage areas due to our advanced and technically superior capture program. To get the most out of Nearmap AI, use it to: Refine and correct your existing data by comparing it with one that is updated frequently and…
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Statistical Performance
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.…
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Confidence Score
We help you make sense of the data and how you can use it by giving each attribute a Confidence score. This score is a representation of how much we believe the classification to be true. Our confidence at detecting the different attributes varies as some are more challenging than others. In a technical sense, these…
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Interpret AI Data based on Object Size
Minimum Object Size For Gen3+ data, no minimum object size filters are applied. The smallest features tend to be the most likely to be false positives, but a customer filtering for a minimum "confidence" of e.g. 70% or 90% is a better filter for false positives than size unless there is a meaningful minimum size that…
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Compare Data Sets
Typically, you have an existing source of truth. For example, a solar installer will have data on the location of solar array installations, which is sourced from smart meters, government, utility installers, and so on. These data sources are infrequently updated or gathered just as a one-off manual effort. To refine this…
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How to Use AI Parcel Data
Reducing manual effort If your role is focused on a manual task, consider whether you can use Nearmap AI results to do it more efficiently. Such an approach frees up time to spend on higher value activities such as assessing the quality of the automated results, performing the analysis more frequently, or performing it at…
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Example: Interpreting the Confidence Score
Sample workflow Identify the part of your data that overlaps with Nearmap AI data. For example, the presence of solar panels at an address. Identify a common key on which to merge the data sets (such as a parcel identifier or address centroid) and merge them. Filter for the cases where there's a mismatch between your data…
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Example: Change Detection
Detecting change with AI parcel data One method for reducing errors is to look at simple changes of your parcel data. For example, when a property goes from having no solar panel to having one, a swimming pool is removed, or a new building appears on a vacant lot. This is robust to a number of errors – slight shifts in…