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 greater scale and precision.
For example, you may be manually mapping the change in an estuary over time to assist management of fishery stocks. Yet it is not feasible to keep this data updated in real time optimally via manual effort.
Detecting change
Cityscapes change at a rapid pace, and you may have a strong interest in identifying the areas that are changing. With up to six captures a year in some locations, Nearmap provides updated data for cities on a regular basis. With Nearmap AI parcel data, it is very easy to detect change between two exported data sets. A comparison of exports at two dates will reveal changes due to construction, growth of trees, etc. However, it will also identify any results that changed due to things such as seasonality in vegetation and lighting, or temporary structures such as shade cloths.
In practice, you'll want to first define what you mean by change (a vacant lot having a building appear, or perhaps the change in area of buildings on the lot).
NOTE: Comparing two exports can reduce the data set from say 100,000 properties, to a few thousand with potential changes to inspect.
