Just How Accurate is Your Drone?

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Unmanned Aircraft Systems (UAS) continue to influence the profession of remote sensing and mapping like few things ever have. Advances in computer technology, global positioning, and miniaturization have conspired to remove considerable barriers to entry. Many new practitioners are buying drones and providing these services and data for the first time. Much (not all) of the science and art of photogrammetry is now coded on a chip. These advancements enable new practitioners to provide a greater array of services to new and existing markets than ever before and fosters the misperception that “anyone can do it”.

New practitioners of drone-based remote sensing and mapping need to understand the fundamentals of remote sensing, mapping, and photogrammetry. Typical deliverables like orthophotography, digital elevation models (DEM), contours, cross-sections, and 3D models depend on this understanding. Nescience of these fundamentals is certain to cause considerable pain, financial loss and compromises to public safety. This article introduces the fundamentals of positional accuracy to help new practitioners provide these services consistent with professional accuracy standards.

I have talked with several practitioners that did not know what “ground control” was or how to use it to establish positional accuracy. This lack of familiarity is not uncommon among novices. They may not know that positional accuracy requirements are needed, or that they are often assumed by the client. They may not know how to discuss positional accuracy with their clients, nor how to measure the positional accuracy of their deliverables.

Truth 1: Positional accuracy doesn’t just happen.

Professionals know that an accurate ortho (or DEM or 3D model) can look identical to an inaccurate one. Both products are “pretty” pictures with lots of great detail but one has more intrinsic value for a greater number of uses than the other because it is more accurate. 

Truth 2: Positional accuracy is a product of the entire drone “system” (aircraft, sensors, operation, and processing software) not any single component.

It matters very little what the drone vendor says about the positional accuracy of its products. A combination of factors (and seldom a single factor) affects the positional accuracy of an orthophoto, DEM, or other derivative of remotely sensed data. Poor operation of the best drone can vitiate the positional accuracy of a deliverable. If a drone manufacturer claims their camera is accurate to two pixels for any given ground sample distance (GSD), the resultant positional accuracy for the orthophoto is dependent on each of the following factors. [The list below is not a comprehensive list of error sources but includes the major contributors of error.]

  1. the cameras inherent potential accuracy
  2. the stability of the flight
  3. the quality of the GPS data
  4. the quality of the inertial system (if the drone even uses one),
  5. the quality of the DEM used to make the orthophoto, and
  6. the type and quality of processing of the raw imagery into an orthophoto (this factor alone has several important sources of error from a “raw” to “finished” product)
  7. the number and quality of ground control points

Each factor contributes some error to the ultimate positional accuracy of the final product. The sum of all errors determines the measurable positional accuracy.

Truth #3: Positional accuracy standards exist and are important.

Understanding accuracy and accuracy standards sets your operations apart from others’. The American Society of Photogrammetry and Remote Sensing (ASPRS) is the major “standards body” for this profession. Their Standards for Geospatial Data reflect the realities of new sensors and digital data. They are “scale- and technology-agnostic”. That is, the standards apply to data produced at any scale using any kind of sensor today or tomorrow. They can be used to measure and report the positional accuracy of geospatial deliverables like orthophotography, DEMs, digital surface models, 3D models, contours, topographic mapping, etc.

Deliverables with good, consistent positional accuracy can be an important differentiator for your drone-based remote sensing business. Unfortunately, a main cost driver of geospatial deliverables is positional accuracy. More accurate data will generally be more expensive than less accurate data. Profitability is highest when the required accuracy is not “over-engineered” and drives up costs.

Truth #4: Best possible positional accuracy today has error of 1 to 1.5 pixels (RMSE).

What level of positional accuracy is achievable using today’s drone systems? Assuming “best practices” with a drone using a metric camera (most drones do NOT have a metric camera), high quality ground control, and solid production procedures (all difficult to achieve consistently) the best possible accuracy for orthos would have a root mean square error (RMSE) = 1 to 1.5 Pixels (GSD). Are these levels of accuracy achievable flying a drone with a non-metric camera and without any ground control?  Not a chance … not today!

Because increasing accuracy comes at a premium it is imperative that the practitioner understand what accuracy is achievable from their drone “system”, what the client expects, and what is needed (this is often at odds with client expectations) to meet the deliverable’s intended use. Because quality remote sensing services are difficult to deliver and need considerable expertise that is not yet programmed into the “easy button” many drone fliers are choosing to collect data and have established firms like Aerial Services produce positionally accurate, irrefragable geospatial deliverables. Please contact us if we can help. Call 319-277-0436 or click here.

Article by Mike Tully, President & CEO of Aerial Services, Inc.

8 comments

  • I think the distinction will become more evident over time. Some clients may want a cheap drone model produced by an amateur with a toy and automatic processing. Others will realize they need an accurate geospatial product, signed off by real, PI insured, industry professionals. There is a big difference.

  • With data from many sources now readily available the need for an informed client is needed evermore. As geospatial professionals we must always try to understand what the client wants and manage their expectations otherwise the difference between a “cheap drone model” and truly geo-referenced one will become blurred.

  • Unfortunately some clients will get bids on a job and take the cheapest bid no matter what.
    While most clients do not really knowing how to compare what is REALLY being offered by Surveyor & Engineers bidding on the project. The accuracy of the Boundary to the Topo and all the other factors involved in a job is important.
    You usually end up getting what you paid for.

  • A very well presented argument! We have been undertaking UAS topographical surveys for over 3 years (the UK was quick to set a legal framework in place). Several months ago we have lost a few jobs to cheaper new entrants to the market. The feed back from those clients was that the “surveys” they received were poor at which point I informed them that that did not surprise me. As a Company we have decided not to chase the price down but to maintain our stance of high quality UAS Surveys pushing “Best Value” rather than “Best Price”. The clients that went elsewhere are now coming back and it seems our stance is beginning to be understood and accepted. Articles like this one all professional UAS surveyors and engineering geologists.

  • Pingback: How Accurate is your Drone | Wisconsin LiDAR Data Users Group

  • Would the title “How accurate is Structure from Motion mapping?” not have been a bit more appropriate? After all, the navigation accuracy of the drone basically only determines how consistently the planned image overlaps turn out. And since most drones in outside applications rely on satellite based augmentation services such as WAAS and EGNOS (with the exception perhaps of Australia and Southern Africa) they are able to navigate with an absolute accuracy of more or less 5 meters. Not exactly very accurate in terms of the desired accuracy of the resulting map, I would say. Far too little is being said about Structure from Motion (SfM) a modeling technique without which mapping with small drones would by no means have been the revolution it has turned out to be. Key factor in small drone mapping is in fact the independence of metric cameras which would typically be too heavy (and too expensive) to be carried by small drones. Using SfM (instead of classical Photogrammetry which critically relies on stability in camera geometry – hence the need for “metric” cameras) with off the shelf consumer grade cameras does indeed yield accuracies in the range of one or two times the magnitude of GSD.

  • Agreed. Hot topic.

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