Meeting industry standards
To talk meaningfully about accuracy, we need a common language. Nearmap adheres to the ASPRS Positional Accuracy Standards for Digital Geospatial Data (Edition 2, Version 2.0). Metrics like Root Mean Square Error (RMSE) ensure we measure, report, and deliver accuracy consistently.
For our 2D imagery, we focus on Horizontal Accuracy (RMSEh). This represents the combined positional error in the horizontal plane (X and Y components), calculated as RMSEh= √ (RMSEx2+RMSEy2). It’s worth noting that older standards often used confidence level metrics like Circular Error 90% (CE90). While we may provide CE90 values for reference, ASPRS Ed2V2 relies solely on RMSE for defining accuracy classes. This shift aims to reduce confusion and improve clarity in how accuracy is reported and understood.
Why ground control points aren’t always needed
While GCPs are critical for certain high-precision, survey-grade projects, they often aren’t needed for common applications. At Nearmap, we use GCPs internally for quality control and validation purposes, but we don’t pass this burden onto our users.
Here’s why you can skip GCPs with Nearmap:
Independent validation: We use GCPs internally to confirm the accuracy of our imagery against ASPRS recommendations, ensuring you get high-quality data without extra steps.
Built-in accuracy: By incorporating positional accuracy directly into capture and processing workflows, Nearmap eliminates the need for users to deploy and manage their own GCPs — saving time and costs.
Out-of-specification handling: If imagery doesn’t hit RMSEh thresholds, it’s flagged. Corrective actions, from reprocessing to data re-acquisition, are used on flagged captures until they meet Nearmap quality standards.
This internal use of GCPs for validation ensures that the accuracy Nearmap states is the accuracy we deliver.