Drone Survey Accuracy Explained | [2024 Update]

In recent years, drone surveying has emerged as a powerful tool for collecting data and producing accurate maps or 3D models of an area.

Drone surveys have become increasingly popular in a variety of industries, from construction to agriculture, due to their safety, efficiency, global accuracy, and cost-effectiveness.

But one of the main worries about drone technology and survey accuracy is that the data collected might not be correct.

Our clients who are interested in our drone mapping data technology frequently ask us this question.

In this blog post, we’ll take a closer look at the factors that affect drone data accuracy and the final accuracy of drone surveys and explore the best practices for ensuring the highest level of accuracy possible.

Whether you’re new to drone surveying or looking to improve your drone survey accuracy, this post will provide you with valuable insights into how to achieve accurate and reliable land surveying results.

How Accurate Is A Drone Survey?

Drone survey accuracy depends on many factors such as drone type, camera quality, flight heigh, ground cover and drone mapping software.

Commercial-grade survey drones can deliver data accuracy to within 0.5cm – 2cm, While other drones may only have an accuracy of 5m. Making them a highly accurate and efficient alternative to traditional surveying methods.

However, achieving this sub-centimeter survey grade accuracy level requires expertise.

Obtaining survey grade accuracy requires careful planning and execution of the drone survey, as well as the use of advanced data processing techniques to correct for errors and distortions in the collected raw data itself.

Defining How Drone Survey Accuracy Is Measured

Before we go over how to ensure accuracy in drone mapping surveys, it is essential to understand how accuracy in drone maps is measured. Drone mapping surveys rely on two types of accuracy:

• Relative Accuracy
• Absolute Accuracy.
• Local Accuracy
• Global Accuracy

Understanding these types of accuracy is essential for achieving accurate results in drone surveying.

Here is a table breaking down these drone accuracy types:

Accuracy Type	Definition
Relative Accuracy	Refers to the precision and consistency of measurements within a dataset, meaning how closely the individual data points are located to each other.
Absolute Accuracy	Refers to how closely the collected data points or measurements match their true, real-world location or true position.
Local Accuracy	Refers to the accuracy of a particular point or area relative to its surrounding features in a local coordinate system.
Global Accuracy	Refers to the overall accuracy of a map or model over a large area or region.

Relative Accuracy:

Relative accuracy refers to the precision and consistency of measurements within a dataset, meaning how closely the individual data points are located to each other.

This is determined by comparing the measurements to a ground control point with a reference frame of a known location.

Relative accuracy is important because it affects the quality and reliability of the dataset as a whole, and any errors or inconsistencies within the dataset can have a significant impact on the accuracy of the final product.

Absolute Accuracy:

Absolute accuracy refers to how closely the collected data points or measurements match their true, real-world location or true position, real-world position itself.

Absolute accuracy is determined by aligning the dataset with a geospatial reference system, such as a coordinate system or a known reference point, through a process called georeferencing.

Absolute accuracy is crucial in drone and aerial surveys too, especially in applications that require precise positionings, such as drone mapping and infrastructure inspections.

Relative accuracy refers to the consistency and precision of measurements of tie points within the same dataset, while absolute accuracy refers to the accuracy of the collected data points in comparison to their actual location in the real world.

High relative and absolute accuracy is crucial in ensuring high-quality drone survey accuracy.

Local Accuracy

Local accuracy refers to the accuracy of a particular point or area relative to its surrounding features in a local coordinate system. It is often used for drone mapping and surveying applications where accuracy is important within a specific area or project.

This can be achieved through the use of ground control points, accurate surveying methods, and high-quality drone mapping software.

Global Accuracy

Global accuracy refers to the overall accuracy of a map or model over a large area or region. It considers the accuracy of the georeferencing process and the relative positions of objects in the map or model, compared to the actual positions on the ground.

Global is expressed as a Root Mean Square Error (RMSE), which measures the difference between the actual and predicted positions of objects.

A low RMSE indicates high accuracy, while a high RMSE indicates low accuracy.

This is important in many applications, such as geographic information systems (GIS), remote sensing, and cartography.

Absolute Accuracy VS Low Relative accuracy

Absolute accuracy and high relative accuracy refer to the accuracy of the drone survey output in relation to a fixed reference point or coordinate system and the accuracy of the measurements relative to each other, respectively.

Local and global refer to the scale of accuracy of the drone survey accuracy and output, with local accuracy referring to a small area and global referring to the entire survey area.

Determining the Accuracy Level for your Project

Determining the accuracy requirements of drone survey data for a client’s project may affect the cost of the project.

Higher data accuracy requirements typically require more resources, such as more ground control points, more precise equipment, and more data processing and analysis time, which can increase the overall cost of the project.

One crucial factor to consider is the intended use of drone survey data. The required accuracy level will depend on the project’s specific needs and the expected end-users needs.

For example, in construction or engineering projects, high accuracy levels are required to ensure that the project meets safety and regulatory requirements.

In contrast, for environmental monitoring projects, lower drone survey absolute accuracy and of levels may be acceptable.

However, it is essential to balance the full vertical accuracy requirements with the project’s budget and the actual position and intended use of the drone survey data.

Choosing the appropriate level of accuracy for the project can help ensure that the project is completed within the client’s budget while still providing the necessary level of accuracy for the project’s specific needs.

Factors Affecting Drone Survey Accuracy Level

Image Overlap

Overlapping images play a crucial role in drone surveying as they help in creating an accurate and detailed model of the surveyed area. During a photogrammetry survey, the drone captures high-resolution photos of the ground. These images need to overlap to create a comprehensive and seamless terrain model.

The overlapping images help in matching the features among the photos to form a single, contiguous model. These images are precisely georeferenced mapping using GPS data.

The accuracy of the drone survey data largely depends on the amount of overlap between the images.

If the overlap between the images is low, there will be gaps and missing information in the final map model, leading to a less accurate representation of the surveyed area.

On the other hand, if the overlap is high, it ensures that the images are well-matched and aligned, leading to a more accurate and detailed survey model.

To ensure the highest level of survey-grade horizontal accuracy ever, a minimum of 70% overlap is required between photos during photogrammetry drone mapping.

This is because photogrammetry software, the process of creating maps and models from photographs, requires a high degree of overlap to match the features of the images and create an accurate model.

This ensured the aerial mapping data has a high degree of vertical accuracy and horizontal accuracy.

Therefore, overlapping images are crucial in drone mapping accuracy and camera quality. This photogrammetry technique is commonly used for construction site as they play a vital role in ensuring the accuracy and quality of the final product.

Ground Sample Distance (GSD)

Ground Sampling Distance (GSD) is an essential factor to consider in drone surveying. GSD refers to the size of a pixel within an image in real-world units, with a GSD of 10 cm meaning that one pixel in the image represents linearly 10 cm on the ground.

It plays a significant role in determining the level of detail that can be captured in the images.

Term	Definition
Ground Sampling Distance (GSD)	Refers to the size of a pixel within an image in real-world units. It represents the linear distance on the ground that one pixel in the image covers.
High GSD	Results in lower resolution imagery, which can lead to less accurate measurements.
Low GSD	Generally results in higher accuracy, as it provides more detailed and accurate imagery for drone mapping and measurement purposes.
Factors affecting GSD	Flight height, camera quality specifications (image width, sensor width, and focal length), and terrain elevation differences.
GSD calculation	GSDh= Flight Height x Sensor Height / Focal Length x image height; GSDw= Flight Height x Sensor Width / Focal Length x Image Width
Importance of GSD	GSD is crucial in determining the level of detail that can be captured in drone surveying imagery, and it affects the accuracy of the measurements. A low GSD is generally preferred for drone mapping and measurement purposes.

A high GSD means that the resolution of the imagery is lower, which can lead to less accurate measurements.

Conversely, a low GSD (e.g., 1 cm) generally results in higher accuracy because it provides more detailed and accurate imagery for drone mapping and measurement purposes.

The GSD is calculated based on the flight height, the camera quality specifications (image width, sensor width, and focal length), and the terrain elevation differences.

The camera quality used in drone surveying can have a significant impact on the Ground Sampling Distance (GSD) and thus, the accuracy of the measurements.

GSDh= Flight Height x Sensor Height / Focal Length x image height

GSDw= Flight Height x Sensor Width / Focal Length x Image Width

In summary, the GSD is a crucial factor to consider in drone surveying as it affects the accuracy of the measurements.

A low GSD is generally preferred for drone mapping and measurement purposes as it provides more detailed and accurate imagery.

Flight Altitude Level

Altitude is an important factor in drone surveying as it affects the Ground Sampling Distance (GSD). A higher altitude results in a higher GSD, meaning that each pixel in the image represents a larger area on the ground.

This can result in less detail and accuracy in the captured aerial images, making it difficult to accurately measure distances and map the area.

However, when surveying uneven terrain, altitude can also have an impact on the GSD variability.

For example, if a drone is flying at a constant altitude over a hilly terrain, the distance between the most point cloud of the drone and the ground will vary, resulting in variable GSD across the images.

This can also affect drone accuracy and the accurate of measurements and drone mapping. The images of a project may not have the same GSD due to terrain elevation differences and changes in the camera’s angle while shooting.

To reduce the impact of variable GSD caused by uneven terrain, drone operators, surveyors, and construction companies can use drones equipped with Terrain Follow Mode.

In this mode, the drone automatically adjusts its altitude to follow the terrain contour and maintain a constant height above the ground.

This helps ensure a consistent GSD across the images and improves the accuracy of the mapping and measurements.

Ground Control Points (GCPs accuracy)

Ground Control Points (GCPs) play a significant role in improving the accuracy of drone surveys. GCPs are markers on the ground with known locations that are used to reference and correct the drone data collected during the survey.

By measuring the GCPs’ low or high relative accuracy due to known locations, the data collected by the drone can be corrected for any errors in positioning and orientation, resulting in a more accurate final product.

To get the best accuracy results when using GCPs, it is crucial to ensure the accuracy of the GCPs themselves.

The accuracy of the GCPs can affect the overall accuracy of the survey, so it’s essential to follow best practices when setting up and measuring GCPs. Achieving centimeter-level accuracy can be challenging if ground control points are not used.

Here are some tips for achieving the best results when using GCPs in drone surveys:

1. Use at least three GCPs per survey. Having multiple GCPs allows for more accurate error correction and helps to ensure the accuracy of the final product.
2. Use GCPs with known and accurate locations. The accuracy of the GCPs should be verified using a survey-grade GPS or other accurate measurement techniques.
3. Place the GCPs in locations that are easily identifiable in the imagery. This will make it easier to accurately measure the GCP locations from the imagery.
4. Use targets that have high contrast and are visible from the air. Targets with high contrast make it easier for the drone’s camera to detect and measure the GCPs.
5. Ensure that the GCPs are level and stable. Any movement or misalignment of the GCPs can result in inaccurate measurements.
6. Measure the GCP locations as accurately as possible.using a survey-grade GPS or other accurate measurement techniques.
7. Record the GCP locations in a format that is compatible with the drone survey software being used. This will make it easier to import the GCP locations into the software and use them to correct the survey drone data.

By following these tips, drone surveyors and construction companies can ensure that their GCPs are accurately measured and used to correct any errors in the survey instruments’ data.

This can result in more accurate and reliable drone survey products.

Survey Software

Survey software plays an important role in drone survey accuracy.

The software is used to process and analyse the data captured by the drone, and the accuracy of the final mapping data depends on the quality of the software used.

To achieve the best accuracy results when using survey software, it is important to use software that is specifically designed for drone surveys.

The software should be able to accurately stitch together the images captured by the drone and produce accurate 3D models or maps of the survey area.

Additionally, the software should have the ability to correct any errors or distortions in the data collected, such as lens distortion, camera tilt, or uneven terrain.

This can be done through a process called georeferencing. A georeferenced map is a type of map that is aligned to real-world geographic coordinates, allowing it to be used in conjunction with other spatial data, such as satellite imagery, GPS data, or other maps.

In other words, georeferencing is the process of assigning real-world coordinates to a map image or dataset so that it can be accurately located within a larger geographic framework.

A precisely georeferenced map is one that has been accurately aligned to its real-world coordinates, typically using ground control points (GCPs) or other surveying methods to ensure a high level of accuracy.

This is particularly important in applications such as land surveying, civil engineering, and environmental monitoring, where precise location information is essential for making informed decisions and avoiding errors.

The software makes sure the data is georeferenced correctly.

This ensures the drone photogrammetry data set is correctly reconstructed.

When a map is georeferenced, it can be used in conjunction with other point cloud data to perform a wide range of tasks, such as measuring distances, calculating areas, or performing spatial analysis.

In addition, georeferenced maps can be used as a base layer for creating other types of maps, such as topographic maps, land-use maps, or vegetation maps.

Overall, the ability to create a precisely georeferenced projects is an essential tool for many applications in modern surveying and mapping, allowing for more accurate and efficient data collection, analysis, and decision-making.

Furthermore, it is important to ensure that the software used is up to date and that the user is familiar with its features and functionalities. This can involve attending training sessions or seeking guidance from experienced professionals in the field.

To achieve the best results, it is important to use software specifically designed for drone surveys, ensure it can correct for errors and distortions, and keep the software up to date while having a good understanding of the software’s features and functionalities.

Flight Conditions

Flight conditions can have a significant impact on accuracy. Unfavorable weather conditions can cause issues such as unstable flights, blurry images, and decreased battery life.

To ensure the best accuracy results, it’s important to understand the effects of weather and how to mitigate them.

Always check the weather conditions before beginning a drone survey. Weather patterns like high winds, fog, or rain can create unstable flight conditions, which in turn can impact the accuracy of the data collected.

In general, calm and clear weather conditions are the best for conducting drone surveys.

When it comes to temperature, keep in mind that extreme heat or cold can impact battery performance, reducing the drone’s flight time and ultimately, the data that can be collected. Be sure to monitor the battery life of the drone throughout the survey and plan flights accordingly.

Humidity is another factor to consider.

High humidity can cause condensation on the drone’s sensors, leading to blurry or distorted images. In humid conditions, it’s important to keep the drone and its sensors dry and properly protected.

In addition to weather conditions, flight planning is also crucial. Plan the survey route to avoid flying over obstacles that could interfere with the drone’s signal, such as tall buildings, deep valleys, or trees.

Always maintain a clear line of sight with the other drone operators and avoid flying in restricted airspace.

Finally, make sure that the drone is properly calibrated before each flight. Calibration helps ensure that the drone is accurately sensing its location, speed, and orientation.

Any errors in calibration can impact the accuracy of the data collected.

Conclusion

In conclusion, drone surveying has become a popular tool for collecting data and producing accurate maps or 3D models of an area.

The accuracy of a drone survey can vary depending on several factors, including the quality of the drone, the sensors it uses, the flight planning, and the data processing methods.

Understanding the different types of accuracy, such as relative and absolute accuracy, local and global accuracy, and the factors that affect drone survey accuracy, such as image overlap, ground sampling distance, flight altitude, ground control points accuracy, and survey software, is crucial to achieving accurate and reliable land surveying results.

By following best practices and using the appropriate level of accuracy for a project, drone surveyors and construction companies can ensure that their data is accurately measured and processed, leading to more accurate and reliable drone survey products.