Accuracy is a critical factor for any kind of site surveying. However, the level of accuracy you need can vary depending on the project. So what does “expected accuracy” mean when we’re talking about drones and sensors, and how can it help you identify the right tools for the job at hand?
In this article, we’re breaking down the expected accuracy ranges for some of the most reliable survey drones on the market. We’ll show you how we calculate expected accuracy—and what these numbers mean to you—so you can confidently select the right hardware for your next project.
What is expected accuracy?
“Expected accuracy” indicates how closely measurements collected by survey hardware, such as a drone, can align with real-world measurements under ideal surveying conditions. It estimates the precision of a terrain model built from that drone’s data compared to verified ground locations.
For example, if a drone has an expected accuracy of 3-5cm, each point in a 3D terrain model generated from that drone’s data is predicted within 3-5 centimeters of the true locations for those points in the physical environment.
Expected accuracy is an important metric because it tells you how much you can trust the precision of your survey data. The level of accuracy you need will vary by project. For instance, a civil contractor laying foundations needs extremely precise measurements, whereas a landfill operator may only need approximate figures to estimate a cell’s remaining airspace or compaction rate.
Expected accuracy for Propeller-compatible drones
Propeller provides an expected accuracy range for each piece of hardware we tested, making it easy to see the level of precision you can expect from your survey data.
Here’s what that looks like:
How Propeller calculates expected accuracy
Where did these expected accuracy figures come from? As you may have guessed, it all starts with a test flight.
To ensure we compare apples to apples, we maintain optimal flight conditions and settings for every drone we test. We fly the same site using the same ground control setup (including AeroPoints) with consistent image overlap and optimized flight altitude based on each drone’s focal length and field of view. We then compare those results against benchmarks from a trusted, high-accuracy PPK drone and known ground control points.
After the test flight, we make three calculations that determine the final accuracy figure:
- Ground Control Points Root Mean Square Error (GCP RMSE)
- GCP-to-surface difference
- Elevation model consistency
Ground control points root mean square error (GCP RMSE)
This figure represents the average difference between the actual locations of ground control points (GCPs) on-site and their estimated positions in the drone’s output model. The lower the RMSE, the more precisely the drone’s model matches real-world coordinates.
GCP RMSE is typically calculated across easting, northing, and elevation to provide a reliable indicator of both horizontal and vertical accuracy.
Elevation model consistency
Finally, we measure a drone’s elevation model consistency by comparing its survey results to a baseline model generated by a trusted, high-accuracy PPK drone. By analyzing the differences in elevation between these two models, we can see whether the tested drone meets our accuracy benchmarks. Consistent results across the same survey area show that a drone can reliably produce survey-grade accuracy.
GCP-to-surface difference
The GCP-to-surface difference reflects the vertical difference between a GCP’s actual elevation and the corresponding points on a drone’s Digital Elevation Model (DEM). A minimal difference shows that the model closely represents the true ground elevation, which is critical for projects that need accurate surface measurements.
For example, if a GCP is at a 200-meter elevation on the actual site, but the surface elevation of the same location for that specific point in the processed model shows 198 meters, the two-meter vertical difference would be the GCP-to-surface difference.
Generating the final expected accuracy range
The last step is to assign a single accuracy range based on the data’s consistency:
- Similar results: If all three measurements yield similar accuracy results (i.e., falling into similar tiers such as 3-5cm), we use this value to represent the expected accuracy for that drone
- Different results: If the GCP-to-surface difference or elevation model consistency reflects lower accuracy than the other components, we adjust the final value accordingly. For example, the Autel EVO II V3 has a 5+cm difference for elevation model consistency, but the GCP RMSE is within 5cm. Our final expected accuracy figure is 5-10cm, which reflects both the DEM margin of error and the higher accuracy values for GCP RMSE
How to use expected accuracy ranges
Site surveys are not created equal, and the level of precision required depends on several factors, including budget, time, and how you’ll use the data. When accuracy is paramount, you need confidence that your drone will provide the best aerial data outputs possible. When time is of the essence, you may just need a quick way to generate rough estimates.
Understanding expected accuracy helps you decide which piece of survey hardware will do the job most effectively. (Psst: Once you’ve completed your survey, data processing is another critical piece of the accuracy puzzle—read more here.)
These ranges are a valuable guide, but if you still need support selecting the best drone for your worksite, we’re here to help! Our global team is available 24/7 to answer your questions and help you achieve your perfect level of accuracy.
Ready to learn how Propeller can power up your worksite with easy and effective data-sharing? Request a demo today.
